From 5c5aa37f99a1810c57de29f62600d980adb58041 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 14:30:25 -0500
Subject: [PATCH 01/16] x8 changes, ppo changes/fixes

---
 assets/x8.xml                             |  20 +++++------
 data/ppo/policies/learner#0.pth           | Bin 0 -> 5090 bytes
 data/ppo/policies/learner#1.pth           | Bin 0 -> 5090 bytes
 data/ppo/stats.txt                        |   0
 data/ppo/trajectories/best_rollout#0.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/best_rollout#1.pkl  | Bin 0 -> 19013 bytes
 data/ppo/trajectories/worst_rollout#0.pkl | Bin 0 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#1.pkl | Bin 0 -> 19020 bytes
 learning/autopilot.py                     |  11 +++---
 learning/ppo.py                           |  42 +++++++++++++++++-----
 simulation/mdp.py                         |  23 ++++++------
 simulation/simulate.py                    |  12 ++++++-
 waypoints.csv                             |  11 +++---
 13 files changed, 75 insertions(+), 44 deletions(-)
 create mode 100644 data/ppo/policies/learner#0.pth
 create mode 100644 data/ppo/policies/learner#1.pth
 create mode 100644 data/ppo/stats.txt
 create mode 100644 data/ppo/trajectories/best_rollout#0.pkl
 create mode 100644 data/ppo/trajectories/best_rollout#1.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#0.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#1.pkl

diff --git a/assets/x8.xml b/assets/x8.xml
index 3368f7b..bcc8bde 100644
--- a/assets/x8.xml
+++ b/assets/x8.xml
@@ -70,10 +70,10 @@
             </location>
             <static_friction> 0.8 </static_friction>
             <dynamic_friction> 0.5 </dynamic_friction>
-            <rolling_friction> 0.1 </rolling_friction>
+            <rolling_friction> .02 </rolling_friction>
             <spring_coeff unit="LBS/FT"> 8.0 </spring_coeff>
-            <damping_coeff unit="LBS/FT/SEC"> 0.85 </damping_coeff>
-            <max_steer unit="DEG"> 0 </max_steer>
+            <damping_coeff unit="LBS/FT/SEC"> 2.85 </damping_coeff>
+            <max_steer unit="DEG"> 360 </max_steer>
             <brake_group> NONE </brake_group>
             <retractable>0</retractable>
         </contact>
@@ -85,10 +85,10 @@
             </location>
             <static_friction> 0.8 </static_friction>
             <dynamic_friction> 0.5 </dynamic_friction>
-            <rolling_friction> 0.1 </rolling_friction>
+            <rolling_friction> 0.03 </rolling_friction>
             <spring_coeff unit="LBS/FT"> 4.0 </spring_coeff>
-            <damping_coeff unit="LBS/FT/SEC"> 0.95 </damping_coeff>
-            <max_steer unit="DEG"> 0.0 </max_steer>
+            <damping_coeff unit="LBS/FT/SEC"> 1.95 </damping_coeff>
+            <max_steer unit="DEG"> 360 </max_steer>
             <brake_group> LEFT </brake_group>
             <retractable>0</retractable>
         </contact>
@@ -100,10 +100,10 @@
             </location>
             <static_friction> 0.8 </static_friction>
             <dynamic_friction> 0.5 </dynamic_friction>
-            <rolling_friction> 0.1 </rolling_friction>
+            <rolling_friction> 0.03 </rolling_friction>
             <spring_coeff unit="LBS/FT"> 4.0 </spring_coeff>
-            <damping_coeff unit="LBS/FT/SEC"> 0.95 </damping_coeff>
-            <max_steer unit="DEG"> 0.0 </max_steer>
+            <damping_coeff unit="LBS/FT/SEC"> 1.95 </damping_coeff>
+            <max_steer unit="DEG"> 360 </max_steer>
             <brake_group> RIGHT </brake_group>
             <retractable>0</retractable>
         </contact>
@@ -145,7 +145,7 @@
           <input> attitude/heading-true-rad </input>
             <!-- <lag> 2500 </lag> -->
             <!-- <noise variation="PERCENT"> .02 </noise> -->
-          <bias> 0.003 </bias>
+          <bias> 0.0 </bias>
         </sensor>
       </channel>
     </system>
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HcmV?d00001

diff --git a/learning/autopilot.py b/learning/autopilot.py
index 5cd3f47..cd4258b 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -50,10 +50,10 @@ def get_control(self, action):
     Clamps various control outputs and sets the mean for control surfaces to 0.
     Assumes [action] is a 4-item tensor of throttle, aileron cmd, elevator cmd, rudder cmd.
     """
-    action[0] = 0.5 * action[0]
+    action[0] = 0.7 * action[0]
     action[1] = 0.1 * (action[1] - 0.5)
-    action[2] = 0.5 * (action[2] - 0.5)
-    action[3] = 0.5 * (action[3] - 0.5) 
+    action[2] = 0.15 * (action[2] - 0.5)
+    action[3] = 0.3 * (action[3] - 0.5) 
     return action
 
   # flattened_params = flattened dx1 numpy array of all params to init from
@@ -125,8 +125,8 @@ def transform_from_deterministic_learner(self):
     self.policy_network[-2] = nn.Linear(self.inputs, self.outputs * 2)
 
     # Update the output layer to include the original weights and biases
-    self.policy_network[-2].weight = nn.Parameter(torch.cat((w, torch.ones(w.shape) * 100), 0))
-    self.policy_network[-2].bias = nn.Parameter(torch.cat((b, torch.ones(b.shape) * 100), 0))
+    self.policy_network[-2].weight = nn.Parameter(torch.cat((w, torch.zeros(w.shape)), 0))
+    self.policy_network[-2].bias = nn.Parameter(torch.cat((b, torch.zeros(b.shape)), 0))
 
     # Add a normal param extractor to the network to extract (means, sigmas) tuple
     self.policy_network.append(NormalParamExtractor())
@@ -153,7 +153,6 @@ def transform_from_deterministic_learner(self):
   def get_action(self, observation):
     data = TensorDict({"observation": observation}, [])
     policy_forward = self.policy_module(data)
-    print("action", self.policy_network(observation))
     return policy_forward["action"], policy_forward["sample_log_prob"]
 
   # NOTE: This initializes from *deterministic* learner parameters and picks
diff --git a/learning/ppo.py b/learning/ppo.py
index e012f67..52fb9da 100644
--- a/learning/ppo.py
+++ b/learning/ppo.py
@@ -10,8 +10,11 @@
 from learning.autopilot import StochasticAutopilotLearner
 from simulation.simulate import FullIntegratedSim
 from simulation.jsbsim_aircraft import x8
+import numpy as np
 import os
 
+device = "cpu" if not torch.has_cuda else "cuda:0"
+
 """
 Gathers rollout data and returns it in the way the Proximal Policy Optimization loss_module expects
 """
@@ -24,15 +27,25 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
   sample_log_probs = torch.empty(0)
   rewards = torch.empty(0)
   dones = torch.empty(0, dtype=torch.bool)
+  best_cum_reward = -float('inf')
+  worst_cum_reward = float('inf')
+  total_cum_reward = 0
   for t in range(num_trajectories):
     integrated_sim = FullIntegratedSim(x8, autopilot_learner, sim_time)
     integrated_sim.simulation_loop()
     
     # Acquire data
     observation, next_observation, action, sample_log_prob, reward, done = integrated_sim.mdp_data_collector.get_trajectory_data()
+    cum_reward = integrated_sim.mdp_data_collector.get_cum_reward()
+    total_cum_reward += cum_reward
     
-    # Save data
-    # integrated_sim.mdp_data_collector.save(os.path.join('ppo', 'trajectories'), 'rollout' + str(policy_num * num_trajectories + t))
+    # Save trajectory if worst or best so far
+    if cum_reward > best_cum_reward:
+      integrated_sim.mdp_data_collector.save(os.path.join('ppo', 'trajectories'), 'best_rollout#' + str(policy_num))
+      best_cum_reward = cum_reward
+    if cum_reward < worst_cum_reward:
+      integrated_sim.mdp_data_collector.save(os.path.join('ppo', 'trajectories'), 'worst_rollout#' + str(policy_num))
+      worst_cum_reward = cum_reward
 
     # Add to the data
     observations = torch.cat((observations, observation))
@@ -55,6 +68,14 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
     ("next", "observation"): next_observations,
   }, [data_size,])
 
+  # Write to stats file
+  stats_file = open(os.path.join('data', 'ppo', 'stats.txt'), 'a')
+  stats_file.write('Policy Number #' + str(policy_num) + ':\n')
+  stats_file.write('Average Reward: ' + str(total_cum_reward/num_trajectories) + '\n')
+  stats_file.write('Best Reward: ' + str(best_cum_reward) + '\n')
+  stats_file.write('Worst Reward: ' + str(worst_cum_reward) + '\n')
+  stats_file.write('\n\n')
+
   return data, data_size
 
 """
@@ -64,12 +85,12 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
 action taken during PPO learning.
 """
 def make_value_estimator_module(n_obs):
+  global device
+
   value_net = nn.Sequential(
-    nn.Linear(n_obs, n_obs),
+    nn.Linear(n_obs, 2*n_obs, device=device),
     nn.Tanh(),
-    nn.Linear(n_obs, n_obs),
-    nn.Tanh(),
-    nn.Linear(n_obs, 1), # one value is computed for the state
+    nn.Linear(2*n_obs, 1, device=device), # one value is computed for the state
   )
 
   return ValueOperator(
@@ -115,7 +136,7 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
     for b in range(0, data_size // batch_size):
       # Gather batch and calculate PPO loss on it
       batch = replay_buffer.sample(batch_size)
-      loss_vals = loss_module(batch.to("cpu"))
+      loss_vals = loss_module(batch.to(device))
       loss_value = (
         loss_vals["loss_objective"]
         + loss_vals["loss_critic"]
@@ -131,17 +152,20 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
 if __name__ == "__main__":
   # Parameters (see https://pytorch.org/rl/tutorials/coding_ppo.html#ppo-parameters)
   batch_size = 100
-  num_epochs = 10
+  num_epochs = 200
+  device = "cpu" if not torch.has_cuda else "cuda:0"
+
   clip_epsilon = 0.2 # for PPO loss
   gamma = 1.0
   lmbda = 0.95
   entropy_eps = 1e-4
   lr = 3e-4
-  num_trajectories = 100
+  num_trajectories = 500
   num_policy_iterations = 100
 
   # Build the modules
   autopilot_learner = StochasticAutopilotLearner()
+  autopilot_learner.init_from_params(np.zeros(238))
   value_module = make_value_estimator_module(autopilot_learner.inputs)
   advantage_module = GAE(
     gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True
diff --git a/simulation/mdp.py b/simulation/mdp.py
index 58a292f..05e16d4 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -117,7 +117,6 @@ def enact_predetermined_controls(sim, autopilot):
 def enact_autopilot(sim, autopilot):
   state = state_from_sim(sim, debug=False)
   action, log_prob = autopilot.get_action(state)
-
   update_sim_from_control(sim, autopilot.get_control(action))
 
   return state, action, log_prob
@@ -152,29 +151,27 @@ def new_init_wp_reward(action, next_state, collided, wp_coeff=1, action_coeff=1,
 
   waypoint_rel_unit = next_state[10:13] / torch.norm(next_state[10:13])
   vel = next_state[1:4]
-  toward_waypoint_reward = wp_coeff * float(torch.dot((vel ** 2 * torch.sign(vel)), waypoint_rel_unit).detach())
+  toward_waypoint_reward = wp_coeff * float(torch.dot(vel, waypoint_rel_unit).detach())
   return toward_waypoint_reward + alt_reward - action_cost if not collided else 0
 
 """
 A reward function.
 """
 def get_wp_reward(sim):
-  def wp_reward(action, next_state, collided, wp_coeff=0.01, action_coeff=0.01, alt_reward_threshold=10):
+  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=0.1):
     if not sim.waypoint_rewarded:
       sim.waypoint_rewarded = True
-      wp_reward = 1_000_000
+      wp_reward = 1_000
     else: wp_reward = 0
-    # print("altitude",  next_state[2] )
-    alt_reward = 0.1 if next_state[2] > alt_reward_threshold else 0
     action_cost = action_coeff * quadratic_action_cost(action)
-
-    waypoint_rel_unit = next_state[10:13] / torch.norm(next_state[10:13])
+    waypoint_rel_unit = torch.nn.functional.normalize(next_state[10:13], dim=0)
     vel = next_state[1:4]
-    toward_waypoint_reward = wp_coeff * float(torch.dot((vel ** 2 * torch.sign(vel)), waypoint_rel_unit).detach())
-    # print("wp_reward: ", wp_reward, " toward_waypoint_reward: ", toward_waypoint_reward)
-    # print("alt_reward: ", alt_reward, " action_cost: ", -action_cost)
-    # print("\t reward", wp_reward + toward_waypoint_reward + alt_reward - action_cost if not collided else 0)
-    return wp_reward + toward_waypoint_reward + alt_reward - action_cost if not collided else 0
+    toward_waypoint_reward = wp_coeff * float(torch.dot(vel, waypoint_rel_unit).detach())
+
+    #print("wp_reward: ", wp_reward, " toward_waypoint_reward: ", toward_waypoint_reward)
+    #print("alt_reward: ", alt_reward, " action_cost: ", -action_cost)
+    #print("\t reward", wp_reward + toward_waypoint_reward + alt_reward - action_cost if not collided else 0)
+    return wp_reward + toward_waypoint_reward - action_cost if not collided else 0
   return wp_reward
 
 """
diff --git a/simulation/simulate.py b/simulation/simulate.py
index b0ab2a3..b4e2c8d 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -2,6 +2,7 @@
 from simulation.jsbsim_aircraft import Aircraft, x8
 import simulation.jsbsim_properties as prp
 from learning.autopilot import AutopilotLearner
+import torch
 import simulation.mdp as mdp
 import os
 import numpy as np
@@ -16,7 +17,7 @@ def __init__(self,
                 autopilot: AutopilotLearner,
                 sim_time: float,
                 display_graphics: bool = True,
-                agent_interaction_frequency: int = 1,
+                agent_interaction_frequency: int = 120,
                 airsim_frequency_hz: float = 392.0,
                 sim_frequency_hz: float = 240.0,
                 init_conditions: bool = None,
@@ -77,6 +78,9 @@ def simulation_loop(self):
       # Do autopilot controls          
       try:
         state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
+        # state, action, log_prob = mdp.enact_predetermined_controls(self.sim, self.autopilot)
+        if torch.isnan(state).any():
+          break
       except Exception as e:
         print(e)
         # If enacting the autopilot fails, end the simulation immediately
@@ -114,6 +118,9 @@ def simulation_loop(self):
       # Get new state
       try:
         next_state = mdp.state_from_sim(self.sim)
+        if torch.isnan(next_state).any():
+          next_state = state
+          self.done = True
       except:
         # If we couldn't acquire the state, something crashed with jsbsim
         # We treat that as the end of the simulation and don't update the state
@@ -130,6 +137,8 @@ def simulation_loop(self):
     self.done = True
     self.mdp_data_collector.terminate(int(i/self.agent_interaction_frequency))
     print('Simulation complete.')
+    print('Cum reward:', self.mdp_data_collector.cum_reward)
+          
 
   """
   Replays a simulation
@@ -151,6 +160,7 @@ def simulation_replay(self, actions):
         # NOTE: for replays, the agent interaction frequency must match what
         # it was when the trajectory was created
         if i % self.agent_interaction_frequency == 0:
+          print("Step")
           break
 
 if __name__ == "__main__":
diff --git a/waypoints.csv b/waypoints.csv
index 7b87c80..07cef09 100644
--- a/waypoints.csv
+++ b/waypoints.csv
@@ -1,7 +1,8 @@
 waypoint_id,x,y,z,relative_x,relative_y,relative_z
 1,-4461,-417.960938,90,21.5,-0.2,0.89
-2,-701,-417.960938,120,59.1,-0.2,1.19
-3,2869,-57.960938,450,94.8,3.4,4.49
-4,6509,-967.960938,1230,131.2,-5.7,12.29
-5,9469,-477.960938,1480,160.8,-0.8,14.79
-6,15619,-397.960938,2920,222.3,0,29.19
\ No newline at end of file
+2,-1891,-487.960938,120,47.2,-0.9,1.19
+3,2626,-461,326,92.37,-0.63039062,3.25
+4,6639,-237.960938,730,132.5,1.6,7.29
+5,15619,-407.960938,2920,222.3,-0.1,29.19
+6,9469,-477.960938,1480,160.8,-0.8,14.79
+7,11919,-287.960938,1980,185.3,1.1,19.79
\ No newline at end of file

From a1628f2dac51a83b87ceeb128ef03be2683eaed1 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 17:52:58 -0500
Subject: [PATCH 02/16] debugging

---
 data/ppo/policies/learner#0.pth           | Bin 5090 -> 5090 bytes
 data/ppo/policies/learner#1.pth           | Bin 5090 -> 5090 bytes
 data/ppo/policies/learner#2.pth           | Bin 0 -> 5090 bytes
 data/ppo/policies/learner#3.pth           | Bin 0 -> 5090 bytes
 data/ppo/policies/learner#4.pth           | Bin 0 -> 5090 bytes
 data/ppo/policies/learner#5.pth           | Bin 0 -> 5090 bytes
 data/ppo/policies/learner#6.pth           | Bin 0 -> 5090 bytes
 data/ppo/policies/learner#7.pth           | Bin 0 -> 5090 bytes
 data/ppo/stats.txt                        |  42 ++++++++++++++++++++++
 data/ppo/trajectories/best_rollout#0.pkl  | Bin 10309 -> 10309 bytes
 data/ppo/trajectories/best_rollout#1.pkl  | Bin 19013 -> 10309 bytes
 data/ppo/trajectories/best_rollout#2.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/best_rollout#3.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/best_rollout#4.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/best_rollout#5.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/best_rollout#6.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/best_rollout#7.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/worst_rollout#0.pkl | Bin 10316 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#1.pkl | Bin 19020 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#2.pkl | Bin 0 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#3.pkl | Bin 0 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#4.pkl | Bin 0 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#5.pkl | Bin 0 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#6.pkl | Bin 0 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#7.pkl | Bin 0 -> 10316 bytes
 learning/autopilot.py                     |  20 ++++-------
 learning/ppo.py                           |   8 +++--
 simulation/jsbsim_simulator.py            |  12 ++++---
 simulation/mdp.py                         |  25 +++++++++----
 simulation/simulate.py                    |  13 +++++--
 waypoints.csv                             |   2 +-
 31 files changed, 91 insertions(+), 31 deletions(-)
 create mode 100644 data/ppo/policies/learner#2.pth
 create mode 100644 data/ppo/policies/learner#3.pth
 create mode 100644 data/ppo/policies/learner#4.pth
 create mode 100644 data/ppo/policies/learner#5.pth
 create mode 100644 data/ppo/policies/learner#6.pth
 create mode 100644 data/ppo/policies/learner#7.pth
 create mode 100644 data/ppo/trajectories/best_rollout#2.pkl
 create mode 100644 data/ppo/trajectories/best_rollout#3.pkl
 create mode 100644 data/ppo/trajectories/best_rollout#4.pkl
 create mode 100644 data/ppo/trajectories/best_rollout#5.pkl
 create mode 100644 data/ppo/trajectories/best_rollout#6.pkl
 create mode 100644 data/ppo/trajectories/best_rollout#7.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#2.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#3.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#4.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#5.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#6.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#7.pkl

diff --git a/data/ppo/policies/learner#0.pth b/data/ppo/policies/learner#0.pth
index 23acc1c55e3a16da23536149d24bebd23e61d816..3fda8db9f661fc0bce4028907c337fbe6b749fab 100644
GIT binary patch
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diff --git a/data/ppo/policies/learner#1.pth b/data/ppo/policies/learner#1.pth
index 954b03ffcff25d3c09f31ebbfe36132c57025e0e..adfa13da066277914a7e05abb7ba995b45db06e3 100644
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diff --git a/data/ppo/policies/learner#2.pth b/data/ppo/policies/learner#2.pth
new file mode 100644
index 0000000000000000000000000000000000000000..a13d1d365a13dbb182b52daef77577fbccb165b8
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diff --git a/data/ppo/policies/learner#3.pth b/data/ppo/policies/learner#3.pth
new file mode 100644
index 0000000000000000000000000000000000000000..e464109d3fc98cc210fed33503d4fb3c43b5c846
GIT binary patch
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diff --git a/data/ppo/policies/learner#4.pth b/data/ppo/policies/learner#4.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f44bc6b64391eb9b937deb61f47767b4aa52c9ab
GIT binary patch
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diff --git a/data/ppo/policies/learner#5.pth b/data/ppo/policies/learner#5.pth
new file mode 100644
index 0000000000000000000000000000000000000000..7b883470fbb01bd7bd7663f41fb90e5033fead61
GIT binary patch
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literal 0
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diff --git a/data/ppo/policies/learner#6.pth b/data/ppo/policies/learner#6.pth
new file mode 100644
index 0000000000000000000000000000000000000000..2f027a036ec07fce8aa28df12942f5c219be7b5d
GIT binary patch
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diff --git a/data/ppo/policies/learner#7.pth b/data/ppo/policies/learner#7.pth
new file mode 100644
index 0000000000000000000000000000000000000000..a03b701486acf050ed28be833afc2a9e982fac61
GIT binary patch
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diff --git a/data/ppo/stats.txt b/data/ppo/stats.txt
index e69de29..f98250b 100644
--- a/data/ppo/stats.txt
+++ b/data/ppo/stats.txt
@@ -0,0 +1,42 @@
+Policy Number #0:
+Average Reward: tensor(1388.7384)
+Best Reward: tensor(2493.6658)
+Worst Reward: tensor(-4.7158)
+
+
+Policy Number #1:
+Average Reward: tensor(1429.3760)
+Best Reward: tensor(2495.7175)
+Worst Reward: tensor(-6.4365)
+
+
+Policy Number #2:
+Average Reward: tensor(1399.7290)
+Best Reward: tensor(2495.4407)
+Worst Reward: tensor(-6.1148)
+
+
+Policy Number #3:
+Average Reward: tensor(1543.7336)
+Best Reward: tensor(3494.7734)
+Worst Reward: tensor(-7.1599)
+
+
+Policy Number #4:
+Average Reward: tensor(1327.8235)
+Best Reward: tensor(2495.4954)
+Worst Reward: tensor(-5.6434)
+
+
+Policy Number #5:
+Average Reward: tensor(1248.0028)
+Best Reward: tensor(2496.1489)
+Worst Reward: tensor(-4.9647)
+
+
+Policy Number #6:
+Average Reward: tensor(1090.4331)
+Best Reward: tensor(2495.0063)
+Worst Reward: tensor(-5.5442)
+
+
diff --git a/data/ppo/trajectories/best_rollout#0.pkl b/data/ppo/trajectories/best_rollout#0.pkl
index 254a622150111c085606d6d5f4926941249a801a..203ffcd3b85dde5bfccd2925cbcbeacecefbc538 100644
GIT binary patch
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diff --git a/data/ppo/trajectories/best_rollout#1.pkl b/data/ppo/trajectories/best_rollout#1.pkl
index 2520b3f38e85d47ea5613f3528c550c356997882..7bbe71bc1c6c1b51ee818fc158eb97cabcaef96d 100644
GIT binary patch
delta 1369
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diff --git a/data/ppo/trajectories/worst_rollout#1.pkl b/data/ppo/trajectories/worst_rollout#1.pkl
index 2d831905bd9482732ba45318f355d017f6dcfa02..fd9424b37b2b682a8f13f31b5e19300e85e73cc9 100644
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diff --git a/data/ppo/trajectories/worst_rollout#2.pkl b/data/ppo/trajectories/worst_rollout#2.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..b1ece6741d620b2e2c107427e6946e808c579755
GIT binary patch
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diff --git a/data/ppo/trajectories/worst_rollout#7.pkl b/data/ppo/trajectories/worst_rollout#7.pkl
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diff --git a/learning/autopilot.py b/learning/autopilot.py
index cd4258b..2f4808e 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -36,7 +36,7 @@ def __init__(self):
       nn.Linear(self.inputs, self.inputs),
       nn.ReLU(),
       nn.Linear(self.inputs, self.outputs),
-      nn.Sigmoid(),
+      nn.Tanh(),
     )
 
   # Returns the action selected and 0, representing the log-prob of the 
@@ -50,10 +50,10 @@ def get_control(self, action):
     Clamps various control outputs and sets the mean for control surfaces to 0.
     Assumes [action] is a 4-item tensor of throttle, aileron cmd, elevator cmd, rudder cmd.
     """
-    action[0] = 0.7 * action[0]
-    action[1] = 0.1 * (action[1] - 0.5)
-    action[2] = 0.15 * (action[2] - 0.5)
-    action[3] = 0.3 * (action[3] - 0.5) 
+    action[0] = 0.8 * (0.5*(action[0] + 1))
+    action[1] = 0.1 * action[1]
+    action[2] = 0.4 * action[2]
+    action[3] = 0.1 * action[3]
     return action
 
   # flattened_params = flattened dx1 numpy array of all params to init from
@@ -82,7 +82,7 @@ def init_from_params(self, flattened_params):
       layer1,
       nn.ReLU(),
       layer2,
-      nn.Sigmoid(),
+      nn.Tanh()
     )
   
   # Loads the network from dir/name.pth
@@ -141,10 +141,6 @@ def transform_from_deterministic_learner(self):
       module=policy_module,
       in_keys=["loc", "scale"],
       distribution_class=TanhNormal,
-      distribution_kwargs={
-          "min": 0, # minimum control
-          "max": 1, # maximum control
-      },
       default_interaction_type=InteractionType.RANDOM,
       return_log_prob=True,
     )
@@ -172,10 +168,6 @@ def init_from_saved(self, path):
       module=policy_module,
       in_keys=["loc", "scale"],
       distribution_class=TanhNormal,
-      distribution_kwargs={
-          "min": 0, # minimum control
-          "max": 1, # maximum control
-      },
       default_interaction_type=InteractionType.RANDOM,
       return_log_prob=True,
     )
diff --git a/learning/ppo.py b/learning/ppo.py
index 52fb9da..43d3209 100644
--- a/learning/ppo.py
+++ b/learning/ppo.py
@@ -145,6 +145,8 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
 
       # Optimize via gradient descent with the optimizer
       loss_value.backward()
+
+      torch.nn.utils.clip_grad_norm_(loss_module.parameters(), 1.0)
       optimizer.step()
       optimizer.zero_grad()
 
@@ -152,15 +154,15 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
 if __name__ == "__main__":
   # Parameters (see https://pytorch.org/rl/tutorials/coding_ppo.html#ppo-parameters)
   batch_size = 100
-  num_epochs = 200
+  num_epochs = 100
   device = "cpu" if not torch.has_cuda else "cuda:0"
 
   clip_epsilon = 0.2 # for PPO loss
   gamma = 1.0
   lmbda = 0.95
   entropy_eps = 1e-4
-  lr = 3e-4
-  num_trajectories = 500
+  lr = 1e-4
+  num_trajectories = 200
   num_policy_iterations = 100
 
   # Build the modules
diff --git a/simulation/jsbsim_simulator.py b/simulation/jsbsim_simulator.py
index 9cc5a11..308f428 100644
--- a/simulation/jsbsim_simulator.py
+++ b/simulation/jsbsim_simulator.py
@@ -84,7 +84,8 @@ def __init__(self,
                  aircraft: Aircraft = x8,
                  init_conditions: Dict[prp.Property, float] = None,
                  debug_level: int = 0):
-        self.fdm = jsbsim.FGFDMExec(root_dir=self.ROOT_DIR)
+        with HidePrints():
+            self.fdm = jsbsim.FGFDMExec(root_dir=self.ROOT_DIR)
         self.fdm.set_debug_level(debug_level)
         self.sim_dt = 1.0 / sim_frequency_hz
         self.aircraft = aircraft
@@ -94,7 +95,9 @@ def __init__(self,
         self.wall_clock_dt = None
         self.update_airsim(ignore_collisions=True)
         self.waypoint_id = 0
-        self.waypoint_threshold = 2
+        self.waypoint_threshold = 3
+        self.takeoff_rewarded = False
+        self.completed_takeoff = False
         self.waypoint_rewarded = True
         self.waypoints = []
         with open("waypoints.csv", 'r') as file:
@@ -241,8 +244,9 @@ def airsim_connect() -> airsim.VehicleClient:
 
         :return: the airsim client object
         """
-        client = airsim.VehicleClient()
-        client.confirmConnection()
+        with HidePrints():
+            client = airsim.VehicleClient()
+            client.confirmConnection()
         return client
 
     def update_airsim(self, ignore_collisions = False) -> None:
diff --git a/simulation/mdp.py b/simulation/mdp.py
index 05e16d4..0239095 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -41,8 +41,13 @@ def state_from_sim(sim, debug=False):
   
   displacement = waypoint - position
 
+  if state[0] >= 2:
+    if not sim.completed_takeoff:
+      print("\t\t\t\t\t\t\t\t\t\tTake off!")
+    sim.completed_takeoff = True
+
   if np.linalg.norm(displacement) <= sim.waypoint_threshold:
-    print("Waypoint Hit!")
+    print(f"\t\t\t\t\t\t\t\t\t\tWaypoint {sim.waypoint_id} Hit!")
     sim.waypoint_id += 1
     sim.waypoint_rewarded = False
 
@@ -124,8 +129,9 @@ def enact_autopilot(sim, autopilot):
 # Takes in the action outputted directly from the network and outputs the 
 # normalized quadratic action cost from 0-1
 def quadratic_action_cost(action):
-  action[1:4] = 2*action[1:4] - 1 # convert control surfaces to [-1, 1]
-  return float(torch.dot(action, action).detach()) / 4 # divide by 4 to be 0-1
+  action_cost_weights = torch.tensor([1.0, 20.0, 10.0, 1.0])
+  action[0] = 0.5 * (action[0] + 1) # converts throttle to be 0-1
+  return float(torch.dot(action ** 2, action_cost_weights).detach()) / sum(action_cost_weights) # divide by 4 to be 0-1
 
 """
 The reward function for the bb autopilots. Since they won't know how to fly, 
@@ -158,7 +164,7 @@ def new_init_wp_reward(action, next_state, collided, wp_coeff=1, action_coeff=1,
 A reward function.
 """
 def get_wp_reward(sim):
-  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=0.1):
+  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=10):
     if not sim.waypoint_rewarded:
       sim.waypoint_rewarded = True
       wp_reward = 1_000
@@ -167,11 +173,18 @@ def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=0.1):
     waypoint_rel_unit = torch.nn.functional.normalize(next_state[10:13], dim=0)
     vel = next_state[1:4]
     toward_waypoint_reward = wp_coeff * float(torch.dot(vel, waypoint_rel_unit).detach())
-
+    toward_waypoint_reward = 0
+    if not sim.takeoff_rewarded and sim.completed_takeoff:
+      takeoff_reward = 500
+      sim.takeoff_rewarded = True
+    else:
+      takeoff_reward = 0
+    # toward_waypoint_reward = 0
+    # toward_waypoint_reward = torch.min(torch.tensor(10), 0.01 * 1 / torch.max(torch.tensor(0.1), torch.sum(next_state[10:13]**2)))
     #print("wp_reward: ", wp_reward, " toward_waypoint_reward: ", toward_waypoint_reward)
     #print("alt_reward: ", alt_reward, " action_cost: ", -action_cost)
     #print("\t reward", wp_reward + toward_waypoint_reward + alt_reward - action_cost if not collided else 0)
-    return wp_reward + toward_waypoint_reward - action_cost if not collided else 0
+    return wp_reward + toward_waypoint_reward + takeoff_reward - action_cost if not collided else 0
   return wp_reward
 
 """
diff --git a/simulation/simulate.py b/simulation/simulate.py
index b4e2c8d..26b8eab 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -1,3 +1,4 @@
+from shared import HidePrints
 from simulation.jsbsim_simulator import Simulation
 from simulation.jsbsim_aircraft import Aircraft, x8
 import simulation.jsbsim_properties as prp
@@ -78,6 +79,12 @@ def simulation_loop(self):
       # Do autopilot controls          
       try:
         state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
+        if i % (self.agent_interaction_frequency * 2) == 0:
+            # print("\t\t\t\t\t\t\t\t\t\tAlt: ", state[0])
+            # print("\n\t\t\t\t\t\t\t\t\t\tControl: ", action)
+            pass
+        #   print("Control\t", action)
+        #   print("Altitude\t", state[0])
         # state, action, log_prob = mdp.enact_predetermined_controls(self.sim, self.autopilot)
         if torch.isnan(state).any():
           break
@@ -104,11 +111,11 @@ def simulation_loop(self):
         # Check for collisions via airsim and terminate if there is one
         if self.sim.get_collision_info().has_collided:
           if self.initial_collision:
-            print('Aircraft has collided.')
+            # print('Aircraft has collided.')
             self.done = True
             self.sim.reinitialize()
           else:
-            print("Aircraft completed initial landing")
+            # print("Aircraft completed initial landing")
             self.initial_collision = True
 
         # Exit if sim is over or it's time for another agent interaction
@@ -137,7 +144,7 @@ def simulation_loop(self):
     self.done = True
     self.mdp_data_collector.terminate(int(i/self.agent_interaction_frequency))
     print('Simulation complete.')
-    print('Cum reward:', self.mdp_data_collector.cum_reward)
+    print('\t\t\t\t\t\t\t\t\t\tCum reward:', self.mdp_data_collector.cum_reward)
           
 
   """
diff --git a/waypoints.csv b/waypoints.csv
index 07cef09..321c2de 100644
--- a/waypoints.csv
+++ b/waypoints.csv
@@ -1,7 +1,7 @@
 waypoint_id,x,y,z,relative_x,relative_y,relative_z
 1,-4461,-417.960938,90,21.5,-0.2,0.89
 2,-1891,-487.960938,120,47.2,-0.9,1.19
-3,2626,-461,326,92.37,-0.63039062,3.25
+3,3006,-461,586,96.17,-0.63039062,5.85
 4,6639,-237.960938,730,132.5,1.6,7.29
 5,15619,-407.960938,2920,222.3,-0.1,29.19
 6,9469,-477.960938,1480,160.8,-0.8,14.79

From 08c73a91fa0ab49a4e0ecdfd129552e856e5f01c Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 17:55:58 -0500
Subject: [PATCH 03/16] waypoints fix

---
 data/ppo/policies/learner#8.pth           | Bin 0 -> 5090 bytes
 data/ppo/stats.txt                        |   6 ++++++
 data/ppo/trajectories/best_rollout#7.pkl  | Bin 10309 -> 10309 bytes
 data/ppo/trajectories/best_rollout#8.pkl  | Bin 0 -> 10309 bytes
 data/ppo/trajectories/worst_rollout#7.pkl | Bin 10316 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#8.pkl | Bin 0 -> 10316 bytes
 waypoints.csv                             |   6 +++---
 7 files changed, 9 insertions(+), 3 deletions(-)
 create mode 100644 data/ppo/policies/learner#8.pth
 create mode 100644 data/ppo/trajectories/best_rollout#8.pkl
 create mode 100644 data/ppo/trajectories/worst_rollout#8.pkl

diff --git a/data/ppo/policies/learner#8.pth b/data/ppo/policies/learner#8.pth
new file mode 100644
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diff --git a/data/ppo/stats.txt b/data/ppo/stats.txt
index f98250b..9c8a96e 100644
--- a/data/ppo/stats.txt
+++ b/data/ppo/stats.txt
@@ -40,3 +40,9 @@ Best Reward: tensor(2495.0063)
 Worst Reward: tensor(-5.5442)
 
 
+Policy Number #7:
+Average Reward: tensor(1055.6771)
+Best Reward: tensor(3493.8289)
+Worst Reward: tensor(-5.2033)
+
+
diff --git a/data/ppo/trajectories/best_rollout#7.pkl b/data/ppo/trajectories/best_rollout#7.pkl
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diff --git a/data/ppo/trajectories/best_rollout#8.pkl b/data/ppo/trajectories/best_rollout#8.pkl
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diff --git a/data/ppo/trajectories/worst_rollout#7.pkl b/data/ppo/trajectories/worst_rollout#7.pkl
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diff --git a/data/ppo/trajectories/worst_rollout#8.pkl b/data/ppo/trajectories/worst_rollout#8.pkl
new file mode 100644
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diff --git a/waypoints.csv b/waypoints.csv
index 321c2de..aa70d84 100644
--- a/waypoints.csv
+++ b/waypoints.csv
@@ -3,6 +3,6 @@ waypoint_id,x,y,z,relative_x,relative_y,relative_z
 2,-1891,-487.960938,120,47.2,-0.9,1.19
 3,3006,-461,586,96.17,-0.63039062,5.85
 4,6639,-237.960938,730,132.5,1.6,7.29
-5,15619,-407.960938,2920,222.3,-0.1,29.19
-6,9469,-477.960938,1480,160.8,-0.8,14.79
-7,11919,-287.960938,1980,185.3,1.1,19.79
\ No newline at end of file
+5,9469,-477.960938,1480,160.8,-0.8,14.79
+6,11919,-287.960938,1980,185.3,1.1,19.79
+7,15619,-407.960938,2920,222.3,-0.1,29.19
\ No newline at end of file

From 2cd548b1946e9d1a69957ccc29dc40a8dd6bf653 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 18:34:28 -0500
Subject: [PATCH 04/16] add total timesteps and unhealthy state
 cost/termination

---
 data/ppo/policies/learner#1.pth           | Bin 5090 -> 5090 bytes
 data/ppo/policies/learner#2.pth           | Bin 5090 -> 5090 bytes
 data/ppo/stats.txt                        |   1 -
 data/ppo/trajectories/best_rollout#0.pkl  | Bin 10309 -> 10309 bytes
 data/ppo/trajectories/best_rollout#1.pkl  | Bin 10309 -> 10309 bytes
 data/ppo/trajectories/best_rollout#2.pkl  | Bin 10309 -> 10309 bytes
 data/ppo/trajectories/worst_rollout#0.pkl | Bin 10316 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#1.pkl | Bin 10316 -> 10316 bytes
 data/ppo/trajectories/worst_rollout#2.pkl | Bin 10316 -> 10316 bytes
 learning/ppo.py                           |   3 +++
 simulation/mdp.py                         |  23 ++++++++++++++++++++--
 simulation/simulate.py                    |  11 +++++++++--
 12 files changed, 33 insertions(+), 5 deletions(-)

diff --git a/data/ppo/policies/learner#1.pth b/data/ppo/policies/learner#1.pth
index adfa13da066277914a7e05abb7ba995b45db06e3..ec555309cc6aa1693e94bd24ed0ac2faf8c49686 100644
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diff --git a/data/ppo/policies/learner#2.pth b/data/ppo/policies/learner#2.pth
index a13d1d365a13dbb182b52daef77577fbccb165b8..95d6eb62dc3e5c3a5645d6dfbc9b9f578324bbc0 100644
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diff --git a/data/ppo/stats.txt b/data/ppo/stats.txt
index 9c8a96e..419332b 100644
--- a/data/ppo/stats.txt
+++ b/data/ppo/stats.txt
@@ -45,4 +45,3 @@ Average Reward: tensor(1055.6771)
 Best Reward: tensor(3493.8289)
 Worst Reward: tensor(-5.2033)
 
-
diff --git a/data/ppo/trajectories/best_rollout#0.pkl b/data/ppo/trajectories/best_rollout#0.pkl
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diff --git a/data/ppo/trajectories/best_rollout#2.pkl b/data/ppo/trajectories/best_rollout#2.pkl
index d165841cfd31ede2a6042b733171ca8a8615c0e3..4c43ba8bffdac0c74a50739cdcaa165e151f1000 100644
GIT binary patch
delta 1458
zcmXApeN>Ej6vt;6rdN@&8MTvAO|2-Ssix=m`#sMX#R`!mR-5)9mMxK#mzh(`#4ORY
zNF0@zUT9-%H8aoidlHq3^-{ZDkB!ilDWQ^Bt=jv?J@=1$?)RSWJ@-=^RvUJ>lxutB
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zALRbV_!uxap?qhIQr6+n(riZeh8Ht-#J-qyf;J_e!%^}_>??6uE~n=QVaGT-cF)PD
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lH;h}2mAi^Jd|S;icf=dsQuFXjF+5G<<dM!BX)g50{||z-jb#7;

delta 1105
zcmYLJZ8V!_7*5vs3eh38lzw0&RdsyC(n#_=@B5<E7-6%v_NDB@Ika6-MthdfR*TcF
zg!K@0SQ(<TNYIk^eG>I?tXf^`bgSth*3Pu5f>SnQhW^<7<39I~`&{R~u5(>?ue4Ws
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Kv9VmstN9PGi}4x&

diff --git a/data/ppo/trajectories/worst_rollout#0.pkl b/data/ppo/trajectories/worst_rollout#0.pkl
index 31863d2ae68e2ee49561a76c793e6a2c028f6fa1..dec4106ffd12ce151037157ee1964abd9854ed13 100644
GIT binary patch
delta 881
zcmV-%1CIR6P|Q$}s{sm;tWE(6k^3|O3X?Da9}1x~K(Pw}000XClTalblXU_AfAq2T
zK39GbKrhk!I<<efzlP%oJ8v~7JVZU{II(Syzx|0gKG3$AKdmyTL68egz9Ib3K8Jw9
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Hld318hEjsd

delta 1163
zcmXApc~DbV6o(UtfI)CUC}pX#B#2c&4Dbx^-uoT_#a1Lj2Zaif!73fac2K~ScES=6
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diff --git a/data/ppo/trajectories/worst_rollout#1.pkl b/data/ppo/trajectories/worst_rollout#1.pkl
index fd9424b37b2b682a8f13f31b5e19300e85e73cc9..91fab992fc2b0fa8c8092c7fefc784710001f749 100644
GIT binary patch
delta 1022
zcmXAoeN0nl6o=cbP}U;gkWWWg!NN=!D<5sa-uHd)4P%;tP=<4;n=lX%lu0&3lt}^C
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delta 1249
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diff --git a/data/ppo/trajectories/worst_rollout#2.pkl b/data/ppo/trajectories/worst_rollout#2.pkl
index b1ece6741d620b2e2c107427e6946e808c579755..b5e2cb22f4fad3ec8073cb9cbd534593ef1fa4fd 100644
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diff --git a/learning/ppo.py b/learning/ppo.py
index 43d3209..7e5c5d5 100644
--- a/learning/ppo.py
+++ b/learning/ppo.py
@@ -30,6 +30,7 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
   best_cum_reward = -float('inf')
   worst_cum_reward = float('inf')
   total_cum_reward = 0
+  total_timesteps = 0
   for t in range(num_trajectories):
     integrated_sim = FullIntegratedSim(x8, autopilot_learner, sim_time)
     integrated_sim.simulation_loop()
@@ -38,6 +39,7 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
     observation, next_observation, action, sample_log_prob, reward, done = integrated_sim.mdp_data_collector.get_trajectory_data()
     cum_reward = integrated_sim.mdp_data_collector.get_cum_reward()
     total_cum_reward += cum_reward
+    total_timesteps += reward.shape[0]
     
     # Save trajectory if worst or best so far
     if cum_reward > best_cum_reward:
@@ -71,6 +73,7 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
   # Write to stats file
   stats_file = open(os.path.join('data', 'ppo', 'stats.txt'), 'a')
   stats_file.write('Policy Number #' + str(policy_num) + ':\n')
+  stats_file.write('Average Time Steps =' + str(total_timesteps/num_trajectories) + ':\n')
   stats_file.write('Average Reward: ' + str(total_cum_reward/num_trajectories) + '\n')
   stats_file.write('Best Reward: ' + str(best_cum_reward) + '\n')
   stats_file.write('Worst Reward: ' + str(worst_cum_reward) + '\n')
diff --git a/simulation/mdp.py b/simulation/mdp.py
index 0239095..28e4dce 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -4,6 +4,7 @@
 states/observations, actions, and rewards.
 """
 
+import math
 import torch
 import simulation.jsbsim_properties as prp
 import numpy as np
@@ -63,8 +64,26 @@ def state_from_sim(sim, debug=False):
     print('RollRate:', state[7], '; PitchRate:', state[8], '; YawRate:', state[9])
     print('Relative WP: (', state[10], state[11], state[12], ')')
 
+  if is_unhealthy_state(state):
+    raise Exception("Unhealthy state, do better")
+
+
   return state
 
+"""
+Returns a bool for whether the state is unhealthy
+"""
+def is_unhealthy_state(state):
+  MAX_BANK =  math.pi / 3
+  MAX_PITCH =  math.pi / 4
+  if np.linalg.norm(state[10:13]) > 75:
+    return True
+  if not -MAX_BANK < state[4] < MAX_BANK:
+    return True
+  if not -MAX_PITCH < state[5] < MAX_PITCH:
+    return True
+  return False
+  
 
 """
 Updates sim according to a control, assumes [control] is a 4-item tensor of
@@ -131,7 +150,7 @@ def enact_autopilot(sim, autopilot):
 def quadratic_action_cost(action):
   action_cost_weights = torch.tensor([1.0, 20.0, 10.0, 1.0])
   action[0] = 0.5 * (action[0] + 1) # converts throttle to be 0-1
-  return float(torch.dot(action ** 2, action_cost_weights).detach()) / sum(action_cost_weights) # divide by 4 to be 0-1
+  return float(torch.dot(action ** 2, action_cost_weights).detach() / sum(action_cost_weights)) # divide by 4 to be 0-1
 
 """
 The reward function for the bb autopilots. Since they won't know how to fly, 
@@ -184,7 +203,7 @@ def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=10):
     #print("wp_reward: ", wp_reward, " toward_waypoint_reward: ", toward_waypoint_reward)
     #print("alt_reward: ", alt_reward, " action_cost: ", -action_cost)
     #print("\t reward", wp_reward + toward_waypoint_reward + alt_reward - action_cost if not collided else 0)
-    return wp_reward + toward_waypoint_reward + takeoff_reward - action_cost if not collided else 0
+    return wp_reward + toward_waypoint_reward + takeoff_reward - action_cost if not collided else -500
   return wp_reward
 
 """
diff --git a/simulation/simulate.py b/simulation/simulate.py
index 26b8eab..d235c82 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -46,6 +46,9 @@ def __init__(self,
     # Triggered when sim is complete
     self.done: bool = False
 
+    # 
+    self.unhealthy_termination: bool = False
+
     self.initial_collision = False
   """
     Run loop for one simulation.
@@ -90,6 +93,7 @@ def simulation_loop(self):
           break
       except Exception as e:
         print(e)
+        self.unhealthy_termination = True
         # If enacting the autopilot fails, end the simulation immediately
         break
       
@@ -128,14 +132,17 @@ def simulation_loop(self):
         if torch.isnan(next_state).any():
           next_state = state
           self.done = True
-      except:
+      except Exception as e:
         # If we couldn't acquire the state, something crashed with jsbsim
         # We treat that as the end of the simulation and don't update the state
+        print("\t\t\t\t\t\t\t\t\t\t", e)
         next_state = state
         self.done = True
+        self.unhealthy_termination = True
 
       # Data collection update for this step
-      self.mdp_data_collector.update(int(i/self.agent_interaction_frequency)-1, state, action, log_prob, next_state, self.done)
+      self.mdp_data_collector.update(int(i/self.agent_interaction_frequency)-1, state, action, 
+                                     log_prob, next_state, self.unhealthy_termination)
 
       # End if collided
       if self.done == True:

From cdd4c26c35791183726f86d7fed81f5d00731946 Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Sun, 3 Dec 2023 20:38:13 -0500
Subject: [PATCH 05/16] Implement a slew rate autopilot learner

---
 learning/autopilot.py  | 95 ++++++++++++++++++++++++++++++++++++++++++
 learning/ppo.py        |  7 ++--
 learning/utils.py      | 11 +++++
 simulation/mdp.py      | 17 ++++++++
 simulation/simulate.py | 18 ++++----
 5 files changed, 134 insertions(+), 14 deletions(-)
 create mode 100644 learning/utils.py

diff --git a/learning/autopilot.py b/learning/autopilot.py
index 2f4808e..c504a8d 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -4,6 +4,9 @@
 from tensordict.nn.distributions import NormalParamExtractor
 from tensordict.nn import TensorDictModule, InteractionType
 from torchrl.modules import ProbabilisticActor, TanhNormal, ValueOperator
+from torch.distributions import Categorical
+from tensordict.nn import CompositeDistribution
+from learning.utils import CategoricalControlsExtractor
 import os
 
 """
@@ -171,3 +174,95 @@ def init_from_saved(self, path):
       default_interaction_type=InteractionType.RANDOM,
       return_log_prob=True,
     )
+
+"""
+Slew rate autopilot learner.
+Each control (throttle/aileron/elevator/rudder) has three options:
+stay constant, go down, or go up.
+"""
+class SlewRateAutopilotLearner:
+  def __init__(self):
+    self.inputs = 13
+    self.outputs = 4
+
+    # Slew rates are wrt sim clock
+    self.throttle_slew_rate = 0.001
+    self.aileron_slew_rate = 0.001
+    self.elevator_slew_rate = 0.001
+    self.rudder_slew_rate = 0.001
+    
+    self.policy_network = nn.Sequential(
+      nn.Linear(self.inputs, self.inputs),
+      nn.ReLU(),
+      nn.Linear(self.inputs, 3 * self.outputs),
+      nn.Sigmoid(),
+      CategoricalControlsExtractor()
+    )
+
+    self.instantiate_policy_module()
+  
+  def instantiate_policy_module(self):
+    policy_module = TensorDictModule(self.policy_network, in_keys=["observation"], out_keys=[("params", "throttle", "probs"),("params", "aileron", "probs"),("params", "elevator", "probs"),("params", "rudder", "probs")])
+    self.policy_module = policy_module = ProbabilisticActor(
+      module=policy_module,
+      in_keys=["params"],
+      distribution_class=CompositeDistribution, 
+      distribution_kwargs={
+        "distribution_map": {
+          "throttle": Categorical,
+          "aileron": Categorical,
+          "elevator": Categorical,
+          "rudder": Categorical,
+        }
+      },
+      default_interaction_type=InteractionType.RANDOM, 
+      return_log_prob=True
+    )
+
+  # Returns the action selected and the log_prob of that action
+  def get_action(self, observation):
+    data = TensorDict({"observation": observation}, [])
+    policy_forward = self.policy_module(data)
+    action = torch.Tensor([policy_forward['throttle'], policy_forward['aileron'], policy_forward['elevator'], policy_forward['rudder']])
+    return action, policy_forward["sample_log_prob"]
+  
+  # Apply a -1 transformation to the action to create control tensor such that:
+  # -1 means go down, 0 means stay same, and +1 means go up
+  def get_controls(self, action):
+    return action - 1
+
+  # flattened_params = flattened dx1 numpy array of all params to init from
+  # NOTE: the way the params are broken up into the weights/biases of each layer
+  #        would need to be manually edited for changes in network architecture
+  def init_from_params(self, flattened_params):
+    flattened_params = torch.from_numpy(flattened_params).to(torch.float32)
+    
+    pl, pr = 0, 0
+    layer1 = nn.Linear(self.inputs, self.inputs)
+    pr += layer1.weight.nelement()
+    layer1.weight = nn.Parameter(flattened_params[pl:pr].reshape(layer1.weight.shape))
+    pl = pr
+    pr += layer1.bias.nelement()
+    layer1.bias = nn.Parameter(flattened_params[pl:pr].reshape(layer1.bias.shape))
+
+    layer2 = nn.Linear(self.inputs, 3*self.outputs)
+    pl = pr
+    pr += layer2.weight.nelement()
+    layer2.weight = nn.Parameter(flattened_params[pl:pr].reshape(layer2.weight.shape))
+    pl = pr
+    pr += layer2.bias.nelement()
+    layer2.bias = nn.Parameter(flattened_params[pl:pr].reshape(layer2.bias.shape))
+
+    self.policy_network = nn.Sequential(
+      layer1,
+      nn.ReLU(),
+      layer2,
+      nn.Sigmoid(),
+      CategoricalControlsExtractor()
+    )
+    self.instantiate_policy_module()
+
+  
+
+  
+
diff --git a/learning/ppo.py b/learning/ppo.py
index 7e5c5d5..063feac 100644
--- a/learning/ppo.py
+++ b/learning/ppo.py
@@ -7,7 +7,7 @@
 from torchrl.data.replay_buffers.storages import LazyTensorStorage
 from torchrl.objectives import ClipPPOLoss
 from torchrl.objectives.value import GAE
-from learning.autopilot import StochasticAutopilotLearner
+from learning.autopilot import StochasticAutopilotLearner, SlewRateAutopilotLearner
 from simulation.simulate import FullIntegratedSim
 from simulation.jsbsim_aircraft import x8
 import numpy as np
@@ -169,8 +169,9 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
   num_policy_iterations = 100
 
   # Build the modules
-  autopilot_learner = StochasticAutopilotLearner()
-  autopilot_learner.init_from_params(np.zeros(238))
+  #autopilot_learner = StochasticAutopilotLearner()
+  autopilot_learner = SlewRateAutopilotLearner()
+  autopilot_learner.init_from_params(np.zeros(350))
   value_module = make_value_estimator_module(autopilot_learner.inputs)
   advantage_module = GAE(
     gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True
diff --git a/learning/utils.py b/learning/utils.py
new file mode 100644
index 0000000..f0f2002
--- /dev/null
+++ b/learning/utils.py
@@ -0,0 +1,11 @@
+import torch
+import torch.nn as nn
+
+class CategoricalControlsExtractor(nn.Module):
+  def __init__(self):
+    super().__init__()
+  
+  def forward(self, *tensors: torch.Tensor) -> tuple[torch.Tensor, ...]:
+    tensor, *others = tensors
+    throttle, aileron, elevator, rudder = tensor.chunk(4, -1)
+    return (nn.functional.softmax(throttle), nn.functional.softmax(aileron), nn.functional.softmax(elevator), nn.functional.softmax(rudder), *others)
diff --git a/simulation/mdp.py b/simulation/mdp.py
index 28e4dce..27e421d 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -96,6 +96,14 @@ def update_sim_from_control(sim, control, debug=False):
   sim[prp.rudder_cmd] = control[3]
   if debug:
     print('Control Taken:', control)
+  
+# Called every single sim step
+# Assumes autopilot is a SlewRateAutopilotLearner
+def update_sim_from_slewrate_control(sim, control, autopilot):
+  sim[prp.throttle_cmd] += control[0] * autopilot.throttle_slew_rate
+  sim[prp.aileron_cmd] += control[1] * autopilot.aileron_slew_rate
+  sim[prp.elevator_cmd] += control[2] * autopilot.elevator_slew_rate
+  sim[prp.rudder_cmd] += control[3] * autopilot.rudder_slew_rate
 
 """
 Follows a predetermined sequence of controls, instead of using autopilot.
@@ -145,6 +153,15 @@ def enact_autopilot(sim, autopilot):
 
   return state, action, log_prob
 
+def query_slewrate_autopilot(sim, autopilot):
+  state = state_from_sim(sim, debug=False)
+  action, log_prob = autopilot.get_action(state)
+  control = autopilot.get_control(action)
+  #update_sim_from_slewrate_control(sim, control)
+
+  return state, action, log_prob, control
+
+
 # Takes in the action outputted directly from the network and outputs the 
 # normalized quadratic action cost from 0-1
 def quadratic_action_cost(action):
diff --git a/simulation/simulate.py b/simulation/simulate.py
index d235c82..70f4ab9 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -2,7 +2,7 @@
 from simulation.jsbsim_simulator import Simulation
 from simulation.jsbsim_aircraft import Aircraft, x8
 import simulation.jsbsim_properties as prp
-from learning.autopilot import AutopilotLearner
+from learning.autopilot import AutopilotLearner, SlewRateAutopilotLearner
 import torch
 import simulation.mdp as mdp
 import os
@@ -15,7 +15,7 @@
 class FullIntegratedSim:
   def __init__(self,
                 aircraft: Aircraft,
-                autopilot: AutopilotLearner,
+                autopilot: SlewRateAutopilotLearner,
                 sim_time: float,
                 display_graphics: bool = True,
                 agent_interaction_frequency: int = 120,
@@ -81,14 +81,8 @@ def simulation_loop(self):
     while i < update_num:
       # Do autopilot controls          
       try:
-        state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
-        if i % (self.agent_interaction_frequency * 2) == 0:
-            # print("\t\t\t\t\t\t\t\t\t\tAlt: ", state[0])
-            # print("\n\t\t\t\t\t\t\t\t\t\tControl: ", action)
-            pass
-        #   print("Control\t", action)
-        #   print("Altitude\t", state[0])
-        # state, action, log_prob = mdp.enact_predetermined_controls(self.sim, self.autopilot)
+        #state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
+        state, action, log_prob, control = mdp.query_slewrate_autopilot(self.sim, self.autopilot)
         if torch.isnan(state).any():
           break
       except Exception as e:
@@ -99,6 +93,8 @@ def simulation_loop(self):
       
       # Update sim while waiting for next agent interaction
       while True:
+        mdp.update_sim_from_slewrate_control(self.sim, control, autopilot)
+
         # Run another sim step
         self.sim.run()
 
@@ -125,7 +121,7 @@ def simulation_loop(self):
         # Exit if sim is over or it's time for another agent interaction
         if self.done or i % self.agent_interaction_frequency == 0:
           break
-      
+        
       # Get new state
       try:
         next_state = mdp.state_from_sim(self.sim)

From 6c74a015a80358db67ce0c7f884122dc0d3cf05b Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Sun, 3 Dec 2023 20:39:52 -0500
Subject: [PATCH 06/16] allow save

---
 learning/autopilot.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/learning/autopilot.py b/learning/autopilot.py
index c504a8d..52bc481 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -261,6 +261,11 @@ def init_from_params(self, flattened_params):
       CategoricalControlsExtractor()
     )
     self.instantiate_policy_module()
+  
+  # Saves the network to dir/name.pth
+  def save(self, dir, name):
+    path = os.path.join(dir, name + '.pth')
+    torch.save(self.policy_network, path)
 
   
 

From fa66d3352fa159d144b4ac40ab8258b754085050 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 20:43:25 -0500
Subject: [PATCH 07/16] debugging slew rate

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 learning/autopilot.py                         |   2 +-
 learning/autotune.py                          |  11 +++++++++++
 pid.json                                      |  18 ++++++++++++++++++
 simulation/simulate.py                        |   4 +++-
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diff --git a/data/cross_entropy/generation1/learner#46.pth b/data/cross_entropy/generation1/learner#46.pth
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diff --git a/data/cross_entropy/generation2/learner#26.pth b/data/cross_entropy/generation2/learner#26.pth
new file mode 100644
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GIT binary patch
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new file mode 100644
index 0000000000000000000000000000000000000000..f108e59b91a7e05b58b5ab9edb78dc1a9839bc31
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diff --git a/data/cross_entropy/generation2/learner#28.pth b/data/cross_entropy/generation2/learner#28.pth
new file mode 100644
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diff --git a/data/cross_entropy/generation2/learner#29.pth b/data/cross_entropy/generation2/learner#29.pth
new file mode 100644
index 0000000000000000000000000000000000000000..df264bc30b99627a528881957b8d0acc4f816c0d
GIT binary patch
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new file mode 100644
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new file mode 100644
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GIT binary patch
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new file mode 100644
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diff --git a/data/cross_entropy/generation2/learner#96.pth b/data/cross_entropy/generation2/learner#96.pth
new file mode 100644
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GIT binary patch
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diff --git a/data/cross_entropy/generation3/learner#4.pth b/data/cross_entropy/generation3/learner#4.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f7aa501f11231cee6c75c6ab6169c4ba53675dec
GIT binary patch
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diff --git a/data/cross_entropy/generation3/learner#40.pth b/data/cross_entropy/generation3/learner#40.pth
new file mode 100644
index 0000000000000000000000000000000000000000..43bb590eae8cf77d542328392e80fee0c4f47a69
GIT binary patch
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diff --git a/data/cross_entropy/generation3/learner#41.pth b/data/cross_entropy/generation3/learner#41.pth
new file mode 100644
index 0000000000000000000000000000000000000000..2fa32c14e8fa6d46deb09b6864fa3b7e4e4c8c79
GIT binary patch
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literal 0
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diff --git a/data/cross_entropy/generation3/learner#42.pth b/data/cross_entropy/generation3/learner#42.pth
new file mode 100644
index 0000000000000000000000000000000000000000..12b38d39adc50bc224ba619362e4e5567a9d8e37
GIT binary patch
literal 3755
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diff --git a/data/cross_entropy/generation3/learner#76.pth b/data/cross_entropy/generation3/learner#76.pth
new file mode 100644
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GIT binary patch
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literal 0
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diff --git a/data/cross_entropy/generation3/learner#77.pth b/data/cross_entropy/generation3/learner#77.pth
new file mode 100644
index 0000000000000000000000000000000000000000..69e761c5c2604c9a9986bdb4a491765a38f3e101
GIT binary patch
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literal 0
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diff --git a/data/cross_entropy/generation3/learner#78.pth b/data/cross_entropy/generation3/learner#78.pth
new file mode 100644
index 0000000000000000000000000000000000000000..b56d7bedd3f291d573d932a8795b5a7034bad602
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/data/cross_entropy/generation3/learner#79.pth b/data/cross_entropy/generation3/learner#79.pth
new file mode 100644
index 0000000000000000000000000000000000000000..2d9a4b8941196bb55903e400b78e3c984133a64c
GIT binary patch
literal 3755
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diff --git a/data/cross_entropy/generation3/learner#99.pth b/data/cross_entropy/generation3/learner#99.pth
new file mode 100644
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diff --git a/data/cross_entropy/stats.txt b/data/cross_entropy/stats.txt
new file mode 100644
index 0000000..bcc8fb9
--- /dev/null
+++ b/data/cross_entropy/stats.txt
@@ -0,0 +1,12 @@
+Generation #1:
+Best, Median, and Worst Learner: (46, 8, 83)
+Best, Median, and Worst Reward: (1991.9085498154163, 991.056861281395, -77.50461518764496)
+
+
+
+Generation #2:
+Best, Median, and Worst Learner: (2, 52, 42)
+Best, Median, and Worst Reward: (1491.1876023560762, 995.0874997675419, -77.25062273442745)
+
+
+
diff --git a/data/cross_entropy_old84085/stats.txt b/data/cross_entropy_old84085/stats.txt
new file mode 100644
index 0000000..e69de29
diff --git a/data/ppo/policies/learner#0.pth b/data/ppo/policies/learner#0.pth
index 3fda8db9f661fc0bce4028907c337fbe6b749fab..f5a03f9724f393936444a6cf981203551c58cb8c 100644
GIT binary patch
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diff --git a/data/ppo/stats.txt b/data/ppo/stats.txt
index 419332b..7dfb2fe 100644
--- a/data/ppo/stats.txt
+++ b/data/ppo/stats.txt
@@ -45,3 +45,10 @@ Average Reward: tensor(1055.6771)
 Best Reward: tensor(3493.8289)
 Worst Reward: tensor(-5.2033)
 
+Policy Number #0:
+Average Time Steps =0.0:
+Average Reward: 0.0
+Best Reward: 0
+Worst Reward: 0
+
+
diff --git a/data/ppo/trajectories/best_rollout#0.pkl b/data/ppo/trajectories/best_rollout#0.pkl
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diff --git a/data/ppo/trajectories/worst_rollout#0.pkl b/data/ppo/trajectories/worst_rollout#0.pkl
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diff --git a/learning/autopilot.py b/learning/autopilot.py
index 52bc481..7466fa2 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -228,7 +228,7 @@ def get_action(self, observation):
   
   # Apply a -1 transformation to the action to create control tensor such that:
   # -1 means go down, 0 means stay same, and +1 means go up
-  def get_controls(self, action):
+  def get_control(self, action):
     return action - 1
 
   # flattened_params = flattened dx1 numpy array of all params to init from
diff --git a/learning/autotune.py b/learning/autotune.py
new file mode 100644
index 0000000..c146a55
--- /dev/null
+++ b/learning/autotune.py
@@ -0,0 +1,11 @@
+
+from learning.autopilot import StochasticPIDControlLearner
+from simulation.simulate import FullIntegratedSim
+from simulation.jsbsim_aircraft import x8
+
+
+if __name__ == "__main__":
+  # A one-minute simulation with an untrained autopilot
+  autopilot = StochasticPIDControlLearner(dt = 1/240)
+  integrated_sim = FullIntegratedSim(x8, autopilot, 60.0, state_dim=13, action_dim=3)
+  integrated_sim.simulation_loop()
\ No newline at end of file
diff --git a/pid.json b/pid.json
new file mode 100644
index 0000000..bc06e52
--- /dev/null
+++ b/pid.json
@@ -0,0 +1,18 @@
+{
+    "speed": {
+        "P": 0.001,
+        "I": 0.001,
+        "D": 0.0001,
+        "B": 0.0
+    },
+    "roll": {
+        "P": 0.00001,
+        "I": 0.00001,
+        "D": 0.00001
+    },
+    "pitch": {
+        "P": 1.0,
+        "I": 0.001,
+        "D": 10.1
+    }
+}
\ No newline at end of file
diff --git a/simulation/simulate.py b/simulation/simulate.py
index 70f4ab9..0ee482b 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -83,6 +83,8 @@ def simulation_loop(self):
       try:
         #state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
         state, action, log_prob, control = mdp.query_slewrate_autopilot(self.sim, self.autopilot)
+        print("\t\t\t\t\t\t\tControl!", control)
+        print("\t\t\t\t\t\t\tAction!", action)
         if torch.isnan(state).any():
           break
       except Exception as e:
@@ -93,7 +95,7 @@ def simulation_loop(self):
       
       # Update sim while waiting for next agent interaction
       while True:
-        mdp.update_sim_from_slewrate_control(self.sim, control, autopilot)
+        mdp.update_sim_from_slewrate_control(self.sim, control, self.autopilot)
 
         # Run another sim step
         self.sim.run()

From 75d591c0a284679153243da2b9b4d4fa7c0ce0ed Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 20:43:46 -0500
Subject: [PATCH 08/16] delete learners

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From 298ea10ebdd766a61ae2487ee54519e8d85d3b02 Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Sun, 3 Dec 2023 20:49:43 -0500
Subject: [PATCH 09/16] Implement control clamping

---
 simulation/mdp.py      | 28 +++++++++++++++-------------
 simulation/simulate.py |  3 +--
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/simulation/mdp.py b/simulation/mdp.py
index 27e421d..ebc3322 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -97,13 +97,23 @@ def update_sim_from_control(sim, control, debug=False):
   if debug:
     print('Control Taken:', control)
   
-# Called every single sim step
+
+# Get the state/action/log_prob and control from the slewrate autopilot
+def query_slewrate_autopilot(sim, autopilot):
+  state = state_from_sim(sim, debug=False)
+  action, log_prob = autopilot.get_action(state)
+  control = autopilot.get_control(action)
+
+  return state, action, log_prob, control
+
+# Called every single sim step to enact slewrate
 # Assumes autopilot is a SlewRateAutopilotLearner
 def update_sim_from_slewrate_control(sim, control, autopilot):
-  sim[prp.throttle_cmd] += control[0] * autopilot.throttle_slew_rate
-  sim[prp.aileron_cmd] += control[1] * autopilot.aileron_slew_rate
-  sim[prp.elevator_cmd] += control[2] * autopilot.elevator_slew_rate
-  sim[prp.rudder_cmd] += control[3] * autopilot.rudder_slew_rate
+  sim[prp.throttle_cmd] = np.clip(sim[prp.throttle_cmd] + control[0] * autopilot.throttle_slew_rate, 0, 1)
+  sim[prp.aileron_cmd] = np.clip(sim[prp.aileron_cmd] + control[1] * autopilot.aileron_slew_rate, -1, 1)
+  sim[prp.elevator_cmd] = np.clip(sim[prp.elevator_cmd] + control[2] * autopilot.elevator_slew_rate, -1, 1)
+  sim[prp.rudder_cmd] += np.clip(sim[prp.rudder_cmd] + control[3] * autopilot.rudder_slew_rate, -1, 1)
+  print('Controls:', torch.Tensor([sim[prp.throttle_cmd], sim[prp.aileron_cmd], sim[prp.elevator_cmd], sim[prp.rudder_cmd]]))
 
 """
 Follows a predetermined sequence of controls, instead of using autopilot.
@@ -153,14 +163,6 @@ def enact_autopilot(sim, autopilot):
 
   return state, action, log_prob
 
-def query_slewrate_autopilot(sim, autopilot):
-  state = state_from_sim(sim, debug=False)
-  action, log_prob = autopilot.get_action(state)
-  control = autopilot.get_control(action)
-  #update_sim_from_slewrate_control(sim, control)
-
-  return state, action, log_prob, control
-
 
 # Takes in the action outputted directly from the network and outputs the 
 # normalized quadratic action cost from 0-1
diff --git a/simulation/simulate.py b/simulation/simulate.py
index 0ee482b..b33a25b 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -83,8 +83,7 @@ def simulation_loop(self):
       try:
         #state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
         state, action, log_prob, control = mdp.query_slewrate_autopilot(self.sim, self.autopilot)
-        print("\t\t\t\t\t\t\tControl!", control)
-        print("\t\t\t\t\t\t\tAction!", action)
+        print("\t\t\t\t\t\t\Action!", control)
         if torch.isnan(state).any():
           break
       except Exception as e:

From c7e4143bf32bbdaf55fbc16948d3fbf7e6e86e06 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Sun, 3 Dec 2023 22:37:58 -0500
Subject: [PATCH 10/16] debugging slew rate and ppo bug

---
 data/ppo/policies/learner#0.pth           | Bin 4710 -> 0 bytes
 data/ppo/policies/learner#1.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#2.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#3.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#4.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#5.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#6.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#7.pth           | Bin 5090 -> 0 bytes
 data/ppo/policies/learner#8.pth           | Bin 5090 -> 0 bytes
 data/ppo/stats.txt                        |  54 ----------------------
 data/ppo/trajectories/best_rollout#0.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#1.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#2.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#3.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#4.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#5.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#6.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#7.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/best_rollout#8.pkl  | Bin 10309 -> 0 bytes
 data/ppo/trajectories/worst_rollout#0.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#1.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#2.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#3.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#4.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#5.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#6.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#7.pkl | Bin 10316 -> 0 bytes
 data/ppo/trajectories/worst_rollout#8.pkl | Bin 10316 -> 0 bytes
 learning/autopilot.py                     |   8 ++--
 learning/ppo.py                           |   4 +-
 simulation/mdp.py                         |  11 +++--
 simulation/simulate.py                    |   4 +-
 32 files changed, 14 insertions(+), 67 deletions(-)
 delete mode 100644 data/ppo/policies/learner#0.pth
 delete mode 100644 data/ppo/policies/learner#1.pth
 delete mode 100644 data/ppo/policies/learner#2.pth
 delete mode 100644 data/ppo/policies/learner#3.pth
 delete mode 100644 data/ppo/policies/learner#4.pth
 delete mode 100644 data/ppo/policies/learner#5.pth
 delete mode 100644 data/ppo/policies/learner#6.pth
 delete mode 100644 data/ppo/policies/learner#7.pth
 delete mode 100644 data/ppo/policies/learner#8.pth
 delete mode 100644 data/ppo/trajectories/best_rollout#0.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#1.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#2.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#3.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#4.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#5.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#6.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#7.pkl
 delete mode 100644 data/ppo/trajectories/best_rollout#8.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#0.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#1.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#2.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#3.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#4.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#5.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#6.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#7.pkl
 delete mode 100644 data/ppo/trajectories/worst_rollout#8.pkl

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diff --git a/data/ppo/stats.txt b/data/ppo/stats.txt
index 7dfb2fe..e69de29 100644
--- a/data/ppo/stats.txt
+++ b/data/ppo/stats.txt
@@ -1,54 +0,0 @@
-Policy Number #0:
-Average Reward: tensor(1388.7384)
-Best Reward: tensor(2493.6658)
-Worst Reward: tensor(-4.7158)
-
-
-Policy Number #1:
-Average Reward: tensor(1429.3760)
-Best Reward: tensor(2495.7175)
-Worst Reward: tensor(-6.4365)
-
-
-Policy Number #2:
-Average Reward: tensor(1399.7290)
-Best Reward: tensor(2495.4407)
-Worst Reward: tensor(-6.1148)
-
-
-Policy Number #3:
-Average Reward: tensor(1543.7336)
-Best Reward: tensor(3494.7734)
-Worst Reward: tensor(-7.1599)
-
-
-Policy Number #4:
-Average Reward: tensor(1327.8235)
-Best Reward: tensor(2495.4954)
-Worst Reward: tensor(-5.6434)
-
-
-Policy Number #5:
-Average Reward: tensor(1248.0028)
-Best Reward: tensor(2496.1489)
-Worst Reward: tensor(-4.9647)
-
-
-Policy Number #6:
-Average Reward: tensor(1090.4331)
-Best Reward: tensor(2495.0063)
-Worst Reward: tensor(-5.5442)
-
-
-Policy Number #7:
-Average Reward: tensor(1055.6771)
-Best Reward: tensor(3493.8289)
-Worst Reward: tensor(-5.2033)
-
-Policy Number #0:
-Average Time Steps =0.0:
-Average Reward: 0.0
-Best Reward: 0
-Worst Reward: 0
-
-
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diff --git a/data/ppo/trajectories/worst_rollout#8.pkl b/data/ppo/trajectories/worst_rollout#8.pkl
deleted file mode 100644
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diff --git a/learning/autopilot.py b/learning/autopilot.py
index 7466fa2..5737081 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -186,10 +186,10 @@ def __init__(self):
     self.outputs = 4
 
     # Slew rates are wrt sim clock
-    self.throttle_slew_rate = 0.001
-    self.aileron_slew_rate = 0.001
-    self.elevator_slew_rate = 0.001
-    self.rudder_slew_rate = 0.001
+    self.throttle_slew_rate = 0.02
+    self.aileron_slew_rate = 0.0001
+    self.elevator_slew_rate = 0.0005
+    self.rudder_slew_rate = 0.0001
     
     self.policy_network = nn.Sequential(
       nn.Linear(self.inputs, self.inputs),
diff --git a/learning/ppo.py b/learning/ppo.py
index 063feac..da55b0f 100644
--- a/learning/ppo.py
+++ b/learning/ppo.py
@@ -165,8 +165,8 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
   lmbda = 0.95
   entropy_eps = 1e-4
   lr = 1e-4
-  num_trajectories = 200
-  num_policy_iterations = 100
+  num_trajectories = 4
+  num_policy_iterations = 4
 
   # Build the modules
   #autopilot_learner = StochasticAutopilotLearner()
diff --git a/simulation/mdp.py b/simulation/mdp.py
index ebc3322..46f797c 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -109,11 +109,12 @@ def query_slewrate_autopilot(sim, autopilot):
 # Called every single sim step to enact slewrate
 # Assumes autopilot is a SlewRateAutopilotLearner
 def update_sim_from_slewrate_control(sim, control, autopilot):
-  sim[prp.throttle_cmd] = np.clip(sim[prp.throttle_cmd] + control[0] * autopilot.throttle_slew_rate, 0, 1)
-  sim[prp.aileron_cmd] = np.clip(sim[prp.aileron_cmd] + control[1] * autopilot.aileron_slew_rate, -1, 1)
-  sim[prp.elevator_cmd] = np.clip(sim[prp.elevator_cmd] + control[2] * autopilot.elevator_slew_rate, -1, 1)
-  sim[prp.rudder_cmd] += np.clip(sim[prp.rudder_cmd] + control[3] * autopilot.rudder_slew_rate, -1, 1)
-  print('Controls:', torch.Tensor([sim[prp.throttle_cmd], sim[prp.aileron_cmd], sim[prp.elevator_cmd], sim[prp.rudder_cmd]]))
+  sim[prp.throttle_cmd] = np.clip(sim[prp.throttle_cmd] + control[0] * autopilot.throttle_slew_rate, 0, 0.8)
+  sim[prp.aileron_cmd] = np.clip(sim[prp.aileron_cmd] + control[1] * autopilot.aileron_slew_rate, -0.1, 0.1)
+  sim[prp.elevator_cmd] = np.clip(sim[prp.elevator_cmd] + control[2] * autopilot.elevator_slew_rate, -0.4, 0.4)
+  sim[prp.rudder_cmd] += np.clip(sim[prp.rudder_cmd] + control[3] * autopilot.rudder_slew_rate, -0.1, 0.1)
+  # print('Controls:', torch.Tensor([sim[prp.throttle_cmd], sim[prp.aileron_cmd], sim[prp.elevator_cmd], sim[prp.rudder_cmd]]))
+  print('\t\t\t\t\t\t\t\t\t\t THROTTLE', sim[prp.throttle_cmd])
 
 """
 Follows a predetermined sequence of controls, instead of using autopilot.
diff --git a/simulation/simulate.py b/simulation/simulate.py
index b33a25b..499be71 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -18,7 +18,7 @@ def __init__(self,
                 autopilot: SlewRateAutopilotLearner,
                 sim_time: float,
                 display_graphics: bool = True,
-                agent_interaction_frequency: int = 120,
+                agent_interaction_frequency: int = 60,
                 airsim_frequency_hz: float = 392.0,
                 sim_frequency_hz: float = 240.0,
                 init_conditions: bool = None,
@@ -83,7 +83,7 @@ def simulation_loop(self):
       try:
         #state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
         state, action, log_prob, control = mdp.query_slewrate_autopilot(self.sim, self.autopilot)
-        print("\t\t\t\t\t\t\Action!", control)
+        # print("\t\t\t\t\t\t\Action!", control)
         if torch.isnan(state).any():
           break
       except Exception as e:

From 0ff9ad72b3db56633f2828e6bc3ef84865a90c22 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Mon, 4 Dec 2023 11:31:57 -0500
Subject: [PATCH 11/16] change states

---
 learning/autopilot.py  |   6 +--
 learning/ppo.py        |  24 ++++++---
 simulation/mdp.py      | 118 ++++++++++++++++++++++++++---------------
 simulation/simulate.py |   1 -
 4 files changed, 94 insertions(+), 55 deletions(-)

diff --git a/learning/autopilot.py b/learning/autopilot.py
index 5737081..4ff4b99 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -23,7 +23,7 @@ def __init__(self):
     - 3 angular velocities: w_roll, w_pitch, w_yaw
     - 3d relative position of next waypoint: wx, wy, wz
     """
-    self.inputs = 13
+    self.inputs = 15
 
     """
     Action:
@@ -182,11 +182,11 @@ def init_from_saved(self, path):
 """
 class SlewRateAutopilotLearner:
   def __init__(self):
-    self.inputs = 13
+    self.inputs = 15
     self.outputs = 4
 
     # Slew rates are wrt sim clock
-    self.throttle_slew_rate = 0.02
+    self.throttle_slew_rate = 0.005
     self.aileron_slew_rate = 0.0001
     self.elevator_slew_rate = 0.0005
     self.rudder_slew_rate = 0.0001
diff --git a/learning/ppo.py b/learning/ppo.py
index da55b0f..76138c2 100644
--- a/learning/ppo.py
+++ b/learning/ppo.py
@@ -62,8 +62,19 @@ def gather_rollout_data(autopilot_learner, policy_num, num_trajectories=100, sim
   # that entry for one step in a rollout.
   data = TensorDict({
     "observation": observations,
-    "action": actions.detach(),
+    "action": TensorDict({
+      "throttle": actions[:,0].detach(),
+      "aileron": actions[:,1].detach(),
+      "elevator": actions[:,2].detach(),
+      "rudder": actions[:,3].detach(),
+      "throttle_log_prob": torch.zeros(data_size).detach(),
+      "aileron_log_prob": torch.zeros(data_size).detach(),
+      "elevator_log_prob": torch.zeros(data_size).detach(),
+      "rudder_log_prob": torch.zeros(data_size).detach(),
+      }, [data_size,]),
     "sample_log_prob": sample_log_probs.detach(), # log probability that each action was selected
+    # "sample_log_prob": TensorDict({
+    # }, [data_size,]), # log probability that each action was selected
     ("next", "done"): dones,
     ("next", "terminated"): dones,
     ("next", "reward"): rewards,
@@ -143,7 +154,6 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
       loss_value = (
         loss_vals["loss_objective"]
         + loss_vals["loss_critic"]
-        + loss_vals["loss_entropy"]
       )
 
       # Optimize via gradient descent with the optimizer
@@ -165,13 +175,13 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
   lmbda = 0.95
   entropy_eps = 1e-4
   lr = 1e-4
-  num_trajectories = 4
-  num_policy_iterations = 4
+  num_trajectories = 200
+  num_policy_iterations = 100
 
   # Build the modules
   #autopilot_learner = StochasticAutopilotLearner()
   autopilot_learner = SlewRateAutopilotLearner()
-  autopilot_learner.init_from_params(np.zeros(350))
+  # autopilot_learner.init_from_params(np.random.normal(0, 1, 350))
   value_module = make_value_estimator_module(autopilot_learner.inputs)
   advantage_module = GAE(
     gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True
@@ -180,8 +190,8 @@ def train_ppo_once(policy_num, autopilot_learner, loss_module, advantage_module,
     actor=autopilot_learner.policy_module,
     critic=value_module,
     clip_epsilon=clip_epsilon,
-    entropy_bonus=bool(entropy_eps),
-    entropy_coef=entropy_eps,
+    entropy_bonus=False,
+    entropy_coef=0,
     value_target_key=advantage_module.value_target_key,
     critic_coef=1.0,
     gamma=gamma,
diff --git a/simulation/mdp.py b/simulation/mdp.py
index 46f797c..c474faf 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -13,28 +13,29 @@
 """
 Extracts agent state data from the sim.
 """
-def state_from_sim(sim, debug=False):
-  state = torch.zeros(13,)
+def state_from_sim(sim):
+  state = torch.zeros(15,)
   
   FT_TO_M = 0.3048
   
   # altitude
   state[0] = sim[prp.altitude_sl_ft] * FT_TO_M # z
 
-  # velocity
-  state[1] = sim[prp.v_north_fps] * FT_TO_M # x velocity
-  state[2] = sim[prp.v_east_fps] * FT_TO_M # y velocity
-  state[3] = -sim[prp.v_down_fps] * FT_TO_M # z velocity
-
-  # angles
-  state[4] = sim[prp.roll_rad] # roll
-  state[5] = sim[prp.pitch_rad] # pitch
-  state[6] = sim[prp.heading_rad] # yaw
+  # speed
+  state[1] = FT_TO_M * np.linalg.norm(np.array([sim[prp.v_north_fps], sim[prp.v_east_fps], -sim[prp.v_down_fps]]))
+  
+  # roll
+  state[2] = sim[prp.roll_rad]
 
+  # pitch 
+  state[3] = sim[prp.pitch_rad] 
+  
   # angle rates
-  state[7] = sim[prp.p_radps] # roll rate
-  state[8] = sim[prp.q_radps] # pitch rate
-  state[9] = sim[prp.r_radps] # yaw rate
+  state[4] = sim[prp.p_radps] # roll rate
+  state[5] = sim[prp.q_radps] # pitch rate
+  state[6] = sim[prp.r_radps] # yaw rate
+
+  state[7] = -sim[prp.v_down_fps] * FT_TO_M # z velocity
 
   # next waypoint (relative)
   position = np.array(sim.get_local_position())
@@ -42,6 +43,40 @@ def state_from_sim(sim, debug=False):
   
   displacement = waypoint - position
 
+  # waypoint ground distance
+  state[8] = np.linalg.norm(np.array(displacement[0:2]))
+
+  # waypoint altitude distance
+  state[9] = displacement[2]
+
+  # angles
+  waypoint_heading = np.arctan2(displacement[1], displacement[0]) 
+  heading = sim[prp.heading_rad] # yaw
+
+  state[10] = waypoint_heading - heading
+
+  if state[10] < -math.pi:
+    state[10] += 2 * math.pi 
+  elif state[10] > math.pi:
+    state[10] -= 2* math.pi
+  
+
+  # print("\t\t\t\t\t\t\t\t\t\t YAW", heading/math.pi*180)
+  # print("\t\t\t\t\t\t\t\t\t\t waypoint_heading", waypoint_heading/math.pi*180)
+
+
+  # print("\t\t\t\t\t\t\t\t\t\theading", state[10]/math.pi*180)
+  # print("\t\t\t\t\t\t\t\t\t\tdist", state[8])
+  # print("\t\t\t\t\t\t\t\t\t\theight", state[9])
+  
+
+
+  state[11] = sim[prp.throttle_cmd]
+  state[12] = sim[prp.aileron_cmd]
+  state[13] = sim[prp.elevator_cmd]
+  state[14] = sim[prp.rudder_cmd]
+
+
   if state[0] >= 2:
     if not sim.completed_takeoff:
       print("\t\t\t\t\t\t\t\t\t\tTake off!")
@@ -52,18 +87,6 @@ def state_from_sim(sim, debug=False):
     sim.waypoint_id += 1
     sim.waypoint_rewarded = False
 
-  state[10] = displacement[0]
-  state[11] = displacement[1]
-  state[12] = displacement[2]
-
-  if debug:
-    print('State!')
-    print('Altitude:', state[0])
-    print('Velocity: (', state[1], state[2], state[3], ')')
-    print('Roll:', state[4], '; Pitch:', state[5], '; Yaw:', state[6])
-    print('RollRate:', state[7], '; PitchRate:', state[8], '; YawRate:', state[9])
-    print('Relative WP: (', state[10], state[11], state[12], ')')
-
   if is_unhealthy_state(state):
     raise Exception("Unhealthy state, do better")
 
@@ -76,11 +99,11 @@ def state_from_sim(sim, debug=False):
 def is_unhealthy_state(state):
   MAX_BANK =  math.pi / 3
   MAX_PITCH =  math.pi / 4
-  if np.linalg.norm(state[10:13]) > 75:
+  if np.linalg.norm(state[8:10]) > 75:
     return True
-  if not -MAX_BANK < state[4] < MAX_BANK:
+  if not -MAX_BANK < state[2] < MAX_BANK:
     return True
-  if not -MAX_PITCH < state[5] < MAX_PITCH:
+  if not -MAX_PITCH < state[3] < MAX_PITCH:
     return True
   return False
   
@@ -100,7 +123,7 @@ def update_sim_from_control(sim, control, debug=False):
 
 # Get the state/action/log_prob and control from the slewrate autopilot
 def query_slewrate_autopilot(sim, autopilot):
-  state = state_from_sim(sim, debug=False)
+  state = state_from_sim(sim)
   action, log_prob = autopilot.get_action(state)
   control = autopilot.get_control(action)
 
@@ -112,9 +135,7 @@ def update_sim_from_slewrate_control(sim, control, autopilot):
   sim[prp.throttle_cmd] = np.clip(sim[prp.throttle_cmd] + control[0] * autopilot.throttle_slew_rate, 0, 0.8)
   sim[prp.aileron_cmd] = np.clip(sim[prp.aileron_cmd] + control[1] * autopilot.aileron_slew_rate, -0.1, 0.1)
   sim[prp.elevator_cmd] = np.clip(sim[prp.elevator_cmd] + control[2] * autopilot.elevator_slew_rate, -0.4, 0.4)
-  sim[prp.rudder_cmd] += np.clip(sim[prp.rudder_cmd] + control[3] * autopilot.rudder_slew_rate, -0.1, 0.1)
-  # print('Controls:', torch.Tensor([sim[prp.throttle_cmd], sim[prp.aileron_cmd], sim[prp.elevator_cmd], sim[prp.rudder_cmd]]))
-  print('\t\t\t\t\t\t\t\t\t\t THROTTLE', sim[prp.throttle_cmd])
+  sim[prp.rudder_cmd] = np.clip(sim[prp.rudder_cmd] + control[3] * autopilot.rudder_slew_rate, -0.1, 0.1)
 
 """
 Follows a predetermined sequence of controls, instead of using autopilot.
@@ -158,7 +179,7 @@ def enact_predetermined_controls(sim, autopilot):
 Basically just updates sim throttle / control surfaces according to the autopilot.
 """
 def enact_autopilot(sim, autopilot):
-  state = state_from_sim(sim, debug=False)
+  state = state_from_sim(sim)
   action, log_prob = autopilot.get_action(state)
   update_sim_from_control(sim, autopilot.get_control(action))
 
@@ -172,12 +193,22 @@ def quadratic_action_cost(action):
   action[0] = 0.5 * (action[0] + 1) # converts throttle to be 0-1
   return float(torch.dot(action ** 2, action_cost_weights).detach() / sum(action_cost_weights)) # divide by 4 to be 0-1
 
+# Takes in the action outputted directly from the network and outputs the 
+# normalized quadratic action cost from 0-1
+def quadratic_control_cost(control):
+  control[1] = control[1] * 1/0.1
+  control[2] = control[2] * 1/0.4
+  control[3] = control[3] * 1/0.1
+  control_cost_weights = torch.tensor([1.0, 20.0, 10.0, 1.0])
+  return float(torch.dot(control ** 2, control_cost_weights).detach() / sum(control_cost_weights)) # divide by 4 to be 0-1
+
 """
 The reward function for the bb autopilots. Since they won't know how to fly, 
 they will get reward as follows (for every timestep prior to collision/termination):
   + 1 if moving with some velocity threshold vel_reward_threshold
   + alt_reward_threshold if flying above ground with some threshold alt_reward_threshold
   - action_coeff * the quadratic control cost
+  !!! WARNING: Deprecated
 """
 def bb_reward(action, next_state, collided, alt_reward_coeff=10, action_coeff=1, alt_reward_threshold=2, vel_reward_threshold=1):
   moving_reward = 1 if (next_state[3]**2 + next_state[4]**2 + next_state[5]**2) > vel_reward_threshold else 0
@@ -189,6 +220,7 @@ def bb_reward(action, next_state, collided, alt_reward_coeff=10, action_coeff=1,
 A reward function that tries to incentivize the plane to go towards the waypoint
 at each timestep, while also rewarding for being above ground and penalizing
 for high control effort
+  !!! WARNING: Deprecated
 """
 def new_init_wp_reward(action, next_state, collided, wp_coeff=1, action_coeff=1, alt_reward_threshold=5):
   alt_reward = 1 if next_state[2] > alt_reward_threshold else 0
@@ -200,18 +232,19 @@ def new_init_wp_reward(action, next_state, collided, wp_coeff=1, action_coeff=1,
   return toward_waypoint_reward + alt_reward - action_cost if not collided else 0
 
 """
-A reward function.
+A reward function getter.
 """
 def get_wp_reward(sim):
-  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=10):
+  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=1):
     if not sim.waypoint_rewarded:
       sim.waypoint_rewarded = True
       wp_reward = 1_000
     else: wp_reward = 0
-    action_cost = action_coeff * quadratic_action_cost(action)
-    waypoint_rel_unit = torch.nn.functional.normalize(next_state[10:13], dim=0)
-    vel = next_state[1:4]
-    toward_waypoint_reward = wp_coeff * float(torch.dot(vel, waypoint_rel_unit).detach())
+    action_cost = action_coeff * quadratic_control_cost(next_state[11:15])
+    #print('\t\t\t\t\t\tACTION COST:', action_cost, next_state[11:15])
+    # waypoint_rel_unit = torch.nn.functional.normalize(next_state[10:13], dim=0)
+    # vel = next_state[1:4]
+    # toward_waypoint_reward = wp_coeff * float(torch.dot(vel, waypoint_rel_unit).detach())
     toward_waypoint_reward = 0
     if not sim.takeoff_rewarded and sim.completed_takeoff:
       takeoff_reward = 500
@@ -220,9 +253,6 @@ def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=10):
       takeoff_reward = 0
     # toward_waypoint_reward = 0
     # toward_waypoint_reward = torch.min(torch.tensor(10), 0.01 * 1 / torch.max(torch.tensor(0.1), torch.sum(next_state[10:13]**2)))
-    #print("wp_reward: ", wp_reward, " toward_waypoint_reward: ", toward_waypoint_reward)
-    #print("alt_reward: ", alt_reward, " action_cost: ", -action_cost)
-    #print("\t reward", wp_reward + toward_waypoint_reward + alt_reward - action_cost if not collided else 0)
     return wp_reward + toward_waypoint_reward + takeoff_reward - action_cost if not collided else -500
   return wp_reward
 
@@ -231,7 +261,7 @@ def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=10):
 rollout, including states/actions/rewards the agent experienced.
 """
 class MDPDataCollector:
-  def __init__(self, sim, reward_fn, expected_trajectory_length, state_dim=13, action_dim=4):
+  def __init__(self, sim, reward_fn, expected_trajectory_length, state_dim=15, action_dim=4):
     # parameters
     self.sim = sim
     self.reward_fn = reward_fn
diff --git a/simulation/simulate.py b/simulation/simulate.py
index 499be71..b1650df 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -83,7 +83,6 @@ def simulation_loop(self):
       try:
         #state, action, log_prob = mdp.enact_autopilot(self.sim, self.autopilot)
         state, action, log_prob, control = mdp.query_slewrate_autopilot(self.sim, self.autopilot)
-        # print("\t\t\t\t\t\t\Action!", control)
         if torch.isnan(state).any():
           break
       except Exception as e:

From 02881ee277b3d2d742d1127690706b2b00f8b988 Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Mon, 4 Dec 2023 11:44:09 -0500
Subject: [PATCH 12/16] make compatible with crossentropy

---
 learning/crossentropy.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/learning/crossentropy.py b/learning/crossentropy.py
index 677fcf1..29e0006 100644
--- a/learning/crossentropy.py
+++ b/learning/crossentropy.py
@@ -1,6 +1,6 @@
 import torch
 import numpy as np
-from learning.autopilot import AutopilotLearner
+from learning.autopilot import AutopilotLearner, SlewRateAutopilotLearner
 from simulation.simulate import FullIntegratedSim
 from simulation.jsbsim_aircraft import x8
 import os
@@ -25,7 +25,7 @@ def __init__(self, learners, num_params):
   def init_using_torch_default(generation_size, num_params):
     learners = []
     for i in range(generation_size):
-      learners.append(AutopilotLearner())
+      learners.append(SlewRateAutopilotLearner())
     return Generation(learners, num_params)
 
   # Utilizes [rewards], which contains the reward obtained by each learner as a
@@ -89,12 +89,12 @@ def make_new_generation(mean, cov, generation_size, num_params):
     # Generate each learner from params
     learners = []
     for param_list in selected_params:
-      l = AutopilotLearner()
+      l = SlewRateAutopilotLearner()
       l.init_from_params(param_list)
       learners.append(l)
     return Generation(learners, num_params)
 
-def cross_entropy_train(epochs, generation_size, num_survive, num_params=238, sim_time=60.0, save_dir='cross_entropy'):
+def cross_entropy_train(epochs, generation_size, num_survive, num_params=350, sim_time=60.0, save_dir='cross_entropy'):
   # Create save_dir (and if one already exists, rename it with some rand int)
   if os.path.exists(os.path.join('data', save_dir)):
     os.rename(os.path.join('data', save_dir), os.path.join('data', save_dir + '_old' + str(randint(0, 100000))))
@@ -127,7 +127,7 @@ def cross_entropy_train(epochs, generation_size, num_survive, num_params=238, si
       integrated_sim.simulation_loop()
 
       # Acquire/save data
-      integrated_sim.mdp_data_collector.save(os.path.join(save_dir, 'generation' + str(epoch+1)), 'trajectory_learner#' + str(i+1))
+      #integrated_sim.mdp_data_collector.save(os.path.join(save_dir, 'generation' + str(epoch+1)), 'trajectory_learner#' + str(i+1))
       rewards.append(integrated_sim.mdp_data_collector.get_cum_reward())
       print('Reward for Learner #', id, ': ', integrated_sim.mdp_data_collector.get_cum_reward())
 

From bb8a32939d9aa55ca777662f90acd37b829cf308 Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Mon, 4 Dec 2023 15:07:13 -0500
Subject: [PATCH 13/16] crossentropy size fixes

---
 learning/crossentropy.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/learning/crossentropy.py b/learning/crossentropy.py
index 29e0006..a768312 100644
--- a/learning/crossentropy.py
+++ b/learning/crossentropy.py
@@ -94,7 +94,7 @@ def make_new_generation(mean, cov, generation_size, num_params):
       learners.append(l)
     return Generation(learners, num_params)
 
-def cross_entropy_train(epochs, generation_size, num_survive, num_params=350, sim_time=60.0, save_dir='cross_entropy'):
+def cross_entropy_train(epochs, generation_size, num_survive, num_params=432, sim_time=60.0, save_dir='cross_entropy'):
   # Create save_dir (and if one already exists, rename it with some rand int)
   if os.path.exists(os.path.join('data', save_dir)):
     os.rename(os.path.join('data', save_dir), os.path.join('data', save_dir + '_old' + str(randint(0, 100000))))
@@ -117,7 +117,7 @@ def cross_entropy_train(epochs, generation_size, num_survive, num_params=350, si
     # Evaluate generation through rollouts
     rewards = []
     for i in range(len(generation.learners)):
-      id = str(100*(epoch+1) + (i+1))
+      id = str(1000*(epoch+1) + (i+1))
       learner = generation.learners[i]
       
       # Run simulation to evaluate learner
@@ -149,4 +149,4 @@ def cross_entropy_train(epochs, generation_size, num_survive, num_params=350, si
 if __name__ == "__main__":
   os.environ["JSBSIM_DEBUG"]=str(0)
   # epochs, generation_size, num_survive
-  cross_entropy_train(100, 99, 50)
\ No newline at end of file
+  cross_entropy_train(100, 200, 50)
\ No newline at end of file

From 5d27d65fbecada00b905001ab40fd9dae04de050 Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Mon, 4 Dec 2023 21:54:08 -0500
Subject: [PATCH 14/16] Query deterministic action for xentropy

---
 learning/autopilot.py | 8 +++++++-
 simulation/mdp.py     | 4 ++--
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/learning/autopilot.py b/learning/autopilot.py
index 4ff4b99..97869fb 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -225,7 +225,13 @@ def get_action(self, observation):
     policy_forward = self.policy_module(data)
     action = torch.Tensor([policy_forward['throttle'], policy_forward['aileron'], policy_forward['elevator'], policy_forward['rudder']])
     return action, policy_forward["sample_log_prob"]
-  
+
+  # Always samples the mode of the output for each control
+  def get_deterministic_action(self, observation):
+    throttle_probs, aileron_probs, elevator_probs, rudder_probs = self.policy_network(observation)
+    action = torch.Tensor([torch.argmax(throttle_probs), torch.argmax(aileron_probs), torch.argmax(elevator_probs), torch.argmax(rudder_probs)])
+    return action, 0
+ 
   # Apply a -1 transformation to the action to create control tensor such that:
   # -1 means go down, 0 means stay same, and +1 means go up
   def get_control(self, action):
diff --git a/simulation/mdp.py b/simulation/mdp.py
index c474faf..0591d5b 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -122,9 +122,9 @@ def update_sim_from_control(sim, control, debug=False):
   
 
 # Get the state/action/log_prob and control from the slewrate autopilot
-def query_slewrate_autopilot(sim, autopilot):
+def query_slewrate_autopilot(sim, autopilot, deterministic=True):
   state = state_from_sim(sim)
-  action, log_prob = autopilot.get_action(state)
+  action, log_prob = autopilot.get_deterministic_action(state) if deterministic else autopilot.get_action(state)
   control = autopilot.get_control(action)
 
   return state, action, log_prob, control

From bcf80410ce277c6107560f754d03ec5ba5208103 Mon Sep 17 00:00:00 2001
From: Owen Sorber <ons3@cornell.edu>
Date: Mon, 4 Dec 2023 22:02:04 -0500
Subject: [PATCH 15/16] control-component of state normalized

---
 simulation/mdp.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/simulation/mdp.py b/simulation/mdp.py
index 0591d5b..031af9b 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -71,10 +71,10 @@ def state_from_sim(sim):
   
 
 
-  state[11] = sim[prp.throttle_cmd]
-  state[12] = sim[prp.aileron_cmd]
-  state[13] = sim[prp.elevator_cmd]
-  state[14] = sim[prp.rudder_cmd]
+  state[11] = sim[prp.throttle_cmd] / 0.8
+  state[12] = sim[prp.aileron_cmd] / 0.1
+  state[13] = sim[prp.elevator_cmd] / 0.4
+  state[14] = sim[prp.rudder_cmd] / 0.1
 
 
   if state[0] >= 2:

From 7f6f52b4d222755282beb6a040dfb29f729bf1b8 Mon Sep 17 00:00:00 2001
From: eric <yk742@cornell.edu>
Date: Tue, 5 Dec 2023 13:45:58 -0500
Subject: [PATCH 16/16] delete unused code, slew-rate fixes

---
 data/cross_entropy/stats.txt          | 12 ------------
 data/cross_entropy_old84085/stats.txt |  0
 learning/autopilot.py                 | 12 ++++++++----
 learning/autotune.py                  | 11 -----------
 pid.json                              | 18 ------------------
 scripts/sim_autopilot.py              |  4 ++--
 simulation/mdp.py                     |  7 ++-----
 simulation/simulate.py                |  2 +-
 8 files changed, 13 insertions(+), 53 deletions(-)
 delete mode 100644 data/cross_entropy/stats.txt
 delete mode 100644 data/cross_entropy_old84085/stats.txt
 delete mode 100644 learning/autotune.py
 delete mode 100644 pid.json

diff --git a/data/cross_entropy/stats.txt b/data/cross_entropy/stats.txt
deleted file mode 100644
index bcc8fb9..0000000
--- a/data/cross_entropy/stats.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-Generation #1:
-Best, Median, and Worst Learner: (46, 8, 83)
-Best, Median, and Worst Reward: (1991.9085498154163, 991.056861281395, -77.50461518764496)
-
-
-
-Generation #2:
-Best, Median, and Worst Learner: (2, 52, 42)
-Best, Median, and Worst Reward: (1491.1876023560762, 995.0874997675419, -77.25062273442745)
-
-
-
diff --git a/data/cross_entropy_old84085/stats.txt b/data/cross_entropy_old84085/stats.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/learning/autopilot.py b/learning/autopilot.py
index 97869fb..d20c6ed 100644
--- a/learning/autopilot.py
+++ b/learning/autopilot.py
@@ -88,9 +88,8 @@ def init_from_params(self, flattened_params):
       nn.Tanh()
     )
   
-  # Loads the network from dir/name.pth
-  def init_from_saved(self, dir, name):
-    path = os.path.join(dir, name + '.pth')
+  # Loads the network from path
+  def init_from_saved(self, path):
     self.policy_network = torch.load(path)
 
   # Saves the network to dir/name.pth
@@ -188,7 +187,7 @@ def __init__(self):
     # Slew rates are wrt sim clock
     self.throttle_slew_rate = 0.005
     self.aileron_slew_rate = 0.0001
-    self.elevator_slew_rate = 0.0005
+    self.elevator_slew_rate = 0.00025
     self.rudder_slew_rate = 0.0001
     
     self.policy_network = nn.Sequential(
@@ -268,6 +267,11 @@ def init_from_params(self, flattened_params):
     )
     self.instantiate_policy_module()
   
+  # Loads the network from path
+  def init_from_saved(self, path):
+    self.policy_network = torch.load(path)
+    self.instantiate_policy_module()
+  
   # Saves the network to dir/name.pth
   def save(self, dir, name):
     path = os.path.join(dir, name + '.pth')
diff --git a/learning/autotune.py b/learning/autotune.py
deleted file mode 100644
index c146a55..0000000
--- a/learning/autotune.py
+++ /dev/null
@@ -1,11 +0,0 @@
-
-from learning.autopilot import StochasticPIDControlLearner
-from simulation.simulate import FullIntegratedSim
-from simulation.jsbsim_aircraft import x8
-
-
-if __name__ == "__main__":
-  # A one-minute simulation with an untrained autopilot
-  autopilot = StochasticPIDControlLearner(dt = 1/240)
-  integrated_sim = FullIntegratedSim(x8, autopilot, 60.0, state_dim=13, action_dim=3)
-  integrated_sim.simulation_loop()
\ No newline at end of file
diff --git a/pid.json b/pid.json
deleted file mode 100644
index bc06e52..0000000
--- a/pid.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-    "speed": {
-        "P": 0.001,
-        "I": 0.001,
-        "D": 0.0001,
-        "B": 0.0
-    },
-    "roll": {
-        "P": 0.00001,
-        "I": 0.00001,
-        "D": 0.00001
-    },
-    "pitch": {
-        "P": 1.0,
-        "I": 0.001,
-        "D": 10.1
-    }
-}
\ No newline at end of file
diff --git a/scripts/sim_autopilot.py b/scripts/sim_autopilot.py
index c54daef..060fe23 100644
--- a/scripts/sim_autopilot.py
+++ b/scripts/sim_autopilot.py
@@ -6,7 +6,7 @@
 and rolls out a trajectory in sim for that autopilot learner.
 """
 
-from learning.autopilot import AutopilotLearner
+from learning.autopilot import AutopilotLearner, SlewRateAutopilotLearner
 from simulation.simulate import FullIntegratedSim
 from simulation.jsbsim_aircraft import x8
 import sys
@@ -17,7 +17,7 @@
 file = os.path.join(*(["data"] + policy_file_path.split('/')))
 
 # Initialize autopilot
-autopilot = AutopilotLearner()
+autopilot = SlewRateAutopilotLearner()
 autopilot.init_from_saved(file)
 
 # Play sim
diff --git a/simulation/mdp.py b/simulation/mdp.py
index 031af9b..fbced7e 100644
--- a/simulation/mdp.py
+++ b/simulation/mdp.py
@@ -196,10 +196,7 @@ def quadratic_action_cost(action):
 # Takes in the action outputted directly from the network and outputs the 
 # normalized quadratic action cost from 0-1
 def quadratic_control_cost(control):
-  control[1] = control[1] * 1/0.1
-  control[2] = control[2] * 1/0.4
-  control[3] = control[3] * 1/0.1
-  control_cost_weights = torch.tensor([1.0, 20.0, 10.0, 1.0])
+  control_cost_weights = torch.tensor([1.0, 10.0, 5.0, 1.0])
   return float(torch.dot(control ** 2, control_cost_weights).detach() / sum(control_cost_weights)) # divide by 4 to be 0-1
 
 """
@@ -235,7 +232,7 @@ def new_init_wp_reward(action, next_state, collided, wp_coeff=1, action_coeff=1,
 A reward function getter.
 """
 def get_wp_reward(sim):
-  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=1):
+  def wp_reward(action, next_state, collided, wp_coeff=0.1, action_coeff=0.5):
     if not sim.waypoint_rewarded:
       sim.waypoint_rewarded = True
       wp_reward = 1_000
diff --git a/simulation/simulate.py b/simulation/simulate.py
index b1650df..bbec2c7 100644
--- a/simulation/simulate.py
+++ b/simulation/simulate.py
@@ -18,7 +18,7 @@ def __init__(self,
                 autopilot: SlewRateAutopilotLearner,
                 sim_time: float,
                 display_graphics: bool = True,
-                agent_interaction_frequency: int = 60,
+                agent_interaction_frequency: int = 10,
                 airsim_frequency_hz: float = 392.0,
                 sim_frequency_hz: float = 240.0,
                 init_conditions: bool = None,