Installation

pip3 install -e .

Usage

import gym
import gym_toy

Brief description of the environments

Tabular

• random_switch.py : 2D gridworld where the agent has to press a switch (5th action) in certain cells to get a reward.

Continuous state / discrete action

• gridworld.py : 2D env with sparse rewards.
• dig.py : 2D env where the agent has to dig land (5th action) to find rewards.

Continuous control

• gridworld_continuous.py : 2D env sparse rewards.
• lqr.py : linear-quadratic regulator.
• lqr_sparse.py : state penalty is always -1, except when the agent is close to the goal (distance < 1).
• sparse_car.py : car moving on a 1D plane with sparse reward.
• sparse_navi.py : agent navigating on a 2D env, with linear dynamics and sparse reward.
• pendulum_sparse.py : like gym Pendulum-v0, but reward is sparse.

Multi-objective

• mo_lqr.py : multi-objective linear-quadratic regulator.
• mo_grid.py : multi-objective gridworld (the farther the reward, the higher its value).