gym-grid-driving

The Grid Driving environment is a simple domain featuring simple yet scalable environment for testing various planning and reinforcement learning algorithm.

Grid Driving

The Grid Driving task involves a "simplified" driving through traffic full of vehicles from one point to the other. At each timestep, the car can move either up, down, or stay on the same lane as it automatically moves forward. The accomplishment of the task would give +10 reward, whereas failure would yield 0 rewards. The sparse nature of the goal and its variability makes the environment suitable as an entry point for initial experimentation on scalability as well as long term planning.

Usage

Execute the following command to install the package

pip install -e .

Create the environment this way

import gym
import gym_grid_driving

env = gym.make('GridDriving-v0')

Example:

import numpy as np

state = env.reset()
for i in range(12):
    env.render()
    state, reward, done, info = env.step(np.random.choice(env.actions))

Configuration

Example configuration

import gym
import gym_grid_driving
from gym_grid_driving.envs.grid_driving import LaneSpec, MaskSpec, Point

lanes = [
    LaneSpec(2, [-1, -1]),
    LaneSpec(2, [-2, -1]),
    LaneSpec(3, [-3, -1]),
]

env = gym.make('GridDriving-v0', lanes=lanes, width=8, 
               agent_speed_range=(-3,-1), finish_position=Point(0,1), agent_pos_init=Point(6,1),
               stochasticity=1.0, tensor_state=False, flicker_rate=0.5, mask=MaskSpec('follow', 2), random_seed=13)

actions = env.actions

env.render()

Details

• lanes accepts a list of LaneSpec(cars, speed_range) governing how each lanes should be, with cars being integer and speed_range=[min, max], min and max should also be integer
• width specifies the width of the simulator as expected
• agent_speed_range=[min, max] is the agent speed range which affects the available actions
• Coordinate of the finisih point finish_position
• Coordinate of the agent initial position agent_pos_init
• env.actions is an enum containing all available actions which would change depending on the agent_speed_range
• env.action_space is the OpenAI gym action space that can be sampled, with the definitions defined in env.actions
• Degree of stochasticity stochasticity with 1.0 being fully-stochastic and 0.0 being fully-deterministic
• observation_type which can be either 'state', 'tensor', or 'vector' whether to output state as it is, as 3D tensor [channel, height, width] with channel=[cars, agent, finish_position, occupancy_trails], or vector.
• random_seed to make the environment reproducible
• flicker_rate specifies how often the observation will not be available (blackout)
• mask defines fog of war that accepts MaskSpec(type, radius) with the type being 'follow' or 'random'. Type follow implies the area visibility to follow the agent whereas random will give random visibility (randomized at every step)

Notes:

• To make the simulator deterministic, one just have to set the stochasticity=0.0 or min=max in the car speed_range
• Parking scenario is just a special case where min=max=0 in the car speed_range

Example output (default configuration)

========================================
  F   -   -   -   -   O   -   -   -   -
  -   -   2   1   -   -   -   -   -   -
  -   -   4   3   -   -   -   5   -   <
========================================

Render every step

Start
========================================
  F   -   -   O   -   -   -   -   -   -
  -   -   -   -   -   2   -   -   -   1
  -   -   3   5   -   -   -   4   -   <
========================================
down
========================================
  F   -   O   -   -   -   -   -   -   -
  -   -   2   ~   ~   -   1   ~   ~   -
  -   3   5   -   -   4   ~   -   <   -
========================================
up
========================================
 OF   ~   -   -   -   -   -   -   -   -
  ~   ~   -   1   ~   ~   -   <   -   2
  3   5   -   -   4   -   -   -   -   -
========================================
forward[-3]
========================================
  F   -   -   -   -   -   -   -   -   O
  1   ~   ~   -   <   -   2   ~   ~   -
  ~   -   4   ~   -   -   -   -   3   5
========================================
down
========================================
  F   -   -   -   -   -   -   O   ~   -
  -   -   -   2   ~   ~   -   1   ~   ~
  ~   ~   -   <   -   -   3   5   ~   4
========================================
down
========================================
  F   -   -   -   -   -   O   -   -   -
  2   ~   ~   -   1   ~   ~   -   -   -
  -   -   <   3   ~   5   ~   -   4   -
========================================
up
========================================
  F   -   -   -   O   ~   -   -   -   -
  -  1#   ~   ~   -   -   -   2   ~   ~
  -   -   3   5   ~   4   ~   ~   -   -
========================================

Legend

• <: Agent
• F: Finish point
• Integer: Car
• #: Crashed agent
• -: Empty road
• ~: Occupancy trails