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

Example: 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:

Action space:

Reward function:

Parallel: 4 independent instances of the same environment configuration.

Collecting objects

Example: 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:

Action space:

Reward function:

Parallel: 4 independent instances of the same environment configuration.

Navigating in procedurally generated mazes

Example: 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:

Action space:

Reward function:

Parallel: 4 independent instances of randomly generated environment configurations.

Physical interaction with movable objects

Example: 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:

Action space:

Reward function:

Parallel: 16 independent instances of the same environment configuration.

Reward functions based on line of sight observation of objects.

Example: 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:

Action space:

Reward function:

Parallel: 4 independent instances of the same environment configuration.