Towards Real World Reinforcement Learning
Investigating real-world control data for offline reinforcement learning.
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 Discrete Latent Dynamics for Planning
Investigating learning finite state representation of a world and learning optimal policy via policy iteration.
A collection of environment for multi-agent reinforcement learning research, built on top of openai-gym.