planning

maze-world
Random maze environments with different size and complexity for reinforcement learning and planning research.
maze-world
Learning `n-step' actions for control tasks
Learn a policy that outputs an action as well as the time-step for which the action should be repeated.
Learning `n-step' actions for control tasks
Learning Discrete Latent Dynamics for Planning
Investigating learning finite state representation of a world and learning optimal policy via policy iteration.
Learning Discrete Latent Dynamics for Planning