Using 🤗 Simulate to learn Agent behaviors with Stable-Baselines3

We provide several example RL integrations with the Stable-Baselines3 (LINK) library. To install this dependancy use pip install simulate[sb3].

Including:

Learning to navigate in a simple T-Maze
Collecting objects
Navigating in procedurally generated mazes
Physical interaction with movable objects
Reward functions based on line of sight observation of objects.

Learning to navigate in a simple T-Maze

Example: examples/sb3_basic_maze.py Objective: Navigate to a spherical object in a simple T-Maze. Upon object collection, the environment resets. Actors: An EgoCentric Camera Actor (LINK) equipped with a monocular camera Observation space: - An RGB camera of shape (3, 40, 40) (C, H, W) in uint8 format. Action space: - A discrete action space with 3 possible actions - Turn left 10 degrees - Turn right 10 degrees - Move forward

Reward function:

A dense reward based on improvement in best euclidean distance to the object
A sparse reward of +1 when the object is collected
A timeout penaly of -1 if the agent does not reach the object in 200 time-steps

Parallel: 4 independent instances of the same environment configuration.

Collecting objects

Example: examples/sb3_collectables.py Objective: Collect all 20 objects in a large square room. Actors: An EgoCentric Camera Actor (LINK) equipped with a monocular camera Observation space: - An RGB camera of shape (3, 40, 40) (C, H, W) in uint8 format. Action space: - A discrete action space with 3 possible actions - Turn left 10 degrees - Turn right 10 degrees - Move forward

Reward function:

A sparse reward of +1 when an object is collected
A timeout penaly of -1 if the agent does not reach the object in 500 time-steps

Parallel: 4 independent instances of the same environment configuration.

Navigating in procedurally generated mazes

Example: examples/sb3_procgen.py Objective: Navigate to an object in a 3D maze, when the object is collected the environment resets. Actors: An EgoCentric Camera Actor (LINK) equipped with a monocular camera Observation space: - An RGB camera of shape (3, 40, 40) (C, H, W) in uint8 format. Action space: - A discrete action space with 3 possible actions - Turn left 10 degrees - Turn right 10 degrees - Move forward

Reward function:

A sparse reward of +1 when the object is reached
A timeout penaly of -1 if the agent does not reach the object in 500 time-steps

Parallel: 4 independent instances of randomly generated environment configurations.

Physical interaction with movable objects

Example: examples/sb3_move_boxes.py Objective: Push boxes in a room near to each other. Actors: An EgoCentric Camera Actor (LINK) equipped with a monocular camera Observation space: - An RGB camera of shape (3, 40, 40) (C, H, W) in uint8 format. Action space: - A discrete action space with 3 possible actions - Turn left 10 degrees - Turn right 10 degrees - Move forward

Reward function:

A reward for moving the red and yellow boxes close to eachother
A reward for moving the green and white boxes close to eachother
A timeout penaly of -1 if the agent does not reach the object in 100 time-steps

Parallel: 16 independent instances of the same environment configuration.

Reward functions based on line of sight observation of objects.

Example: examples/sb3_visual_reward.py Objective: Move the agent so the box is within the agents its field of view Actors: An EgoCentric Camera Actor (LINK) equipped with a monocular camera Observation space: - An RGB camera of shape (3, 40, 40) (C, H, W) in uint8 format. Action space: - A discrete action space with 3 possible actions - Turn left 10 degrees - Turn right 10 degrees - Move forward

Reward function:

A sparse reward for moving the box within a 60 degree fov cone in front of the agent.
A timeout penaly of -1 if the agent does not reach the object in 100 time-steps

Parallel: 4 independent instances of the same environment configuration.