theta and gamma results

Author: dantzert

baseline_controllers/epsilon/v1/optimal_constant_flows.txt

@@ -1,11 +1,11 @@
-4.704627765844190179e+00
-3.015805097896647524e+00
-4.368238579733623617e+00
-4.780055633932779102e+00
-4.367912950115156789e+00
-4.146493746603518105e+00
-1.892306233185714559e+00
-4.582047429071768541e+00
-3.618796664291114684e+00
-3.166489557973879432e+00
-4.579939918019401368e+00
+2.999999999999999889e-01
+3.247292591954848096e+00
+2.999999999999999889e-01
+6.704977198373817115e-01
+2.999999999999999889e-01
+4.528406946376854592e+00
+4.386141156671918395e+00
+3.754168154707762550e+00
+4.595462127333146407e-01
+5.219601815475097162e+00
+3.539768189926069564e+00

baseline_controllers/epsilon/v1/optimal_constant_flows_cost.txt

@@ -1 +1 @@
-1.209282765596643294e+05
+1.120910370898277470e+05

baseline_controllers/gamma/evaluate_baseline_controllers.py

@@ -21,7 +21,7 @@
 
 # GAMMA
 # options are: 'equal-filling' and 'constant-flow' (or 'uncontrolled')
-evaluating = 'uncontrolled' 
+evaluating = 'constant-flow' 
 verbose = True
 version = "2" # options are "1" and "2"
 level = "1" # options are "1" , "2", and "3"
@@ -51,11 +51,16 @@
 
 
 for parameter in tuning_values:
+    if evaluating == "constant-flow":
+        optimal_constant_flows = np.loadtxt(str("./v" + version + "/optimal_constant_flows.txt"))
+    elif evaluating == "equal-filling":
+        optimal_constant_flows = np.loadtxt(str("./v" + version + "/optimal_efd.txt"))[:-1]
+        optimal_efd_params = np.loadtxt(str("./v" + version + "/optimal_efd.txt"))[-1]
     #optimal_constant_flows = np.loadtxt(str("./v" + version + "/optimal_constant_flows.txt"))
-    optimal_constant_flows = np.ones(9)*6.0
+    #optimal_constant_flows = np.ones(9)*6.0
     #optimal_constant_flows[[4,8]] = 75.0 # 5 and 9 will flood
     #optimal_efd_params = np.loadtxt(str("./v" + version + "/optimal_efd_params.txt"))
-    optimal_efd_params = 0.5
+    #optimal_efd_params = 0.5
 
     print("tuning value: ", parameter)
     optimal_constant_flows = optimal_constant_flows*(1+parameter)

baseline_controllers/gamma/optimize_parameters.py

@@ -1,10 +1,10 @@
 ,cost,final_depths,final_filling
-0,157984.56953174088,0.02,0.0
-1,157984.56953174088,0.08,0.01
-2,157984.56953174088,0.05,0.0
-3,157984.56953174088,0.05,0.0
-4,157984.56953174088,0.01,0.0
-5,157984.56953174088,0.0,0.0
-6,157984.56953174088,0.0,0.0
-7,157984.56953174088,0.0,0.0
-8,157984.56953174088,0.0,0.0
+0,46815.37457569794,0.02,0.0
+1,46815.37457569794,0.09,0.01
+2,46815.37457569794,0.06,0.01
+3,46815.37457569794,0.06,0.01
+4,46815.37457569794,0.01,0.0
+5,46815.37457569794,0.0,0.0
+6,46815.37457569794,0.0,0.0
+7,46815.37457569794,0.0,0.0
+8,46815.37457569794,0.0,0.0

baseline_controllers/gamma/v2/lev1/results/costs_constant-flow_a=0.0.csv

@@ -1,10 +1,10 @@
 ,cost,final_depths,final_filling
-0,169097.1580202613,0.01,0.0
-1,169097.1580202613,0.07,0.01
-2,169097.1580202613,0.04,0.0
-3,169097.1580202613,0.04,0.0
-4,169097.1580202613,0.01,0.0
-5,169097.1580202613,0.0,0.0
-6,169097.1580202613,0.0,0.0
-7,169097.1580202613,0.0,0.0
-8,169097.1580202613,0.0,0.0
+0,117134.41665265542,0.02,0.0
+1,117134.41665265542,0.07,0.01
+2,117134.41665265542,0.05,0.0
+3,117134.41665265542,0.04,0.0
+4,117134.41665265542,0.01,0.0
+5,117134.41665265542,0.0,0.0
+6,117134.41665265542,0.0,0.0
+7,117134.41665265542,0.0,0.0
+8,117134.41665265542,0.0,0.0