| Definition | Collection of different environments |
| No of environments | 3 |
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).