PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) Emphasizing Recent Experience (ERE) Munchausen RL D2RL and parallel Environments.
-
Updated
Feb 24, 2021 - Python
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) Emphasizing Recent Experience (ERE) Munchausen RL D2RL and parallel Environments.
PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN
PyTorch implementation of D4PG with the SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.
PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3) - including additional Extension to improve the algorithm's performance.
Add a description, image, and links to the munchausen-reinforcement-learning topic page so that developers can more easily learn about it.
To associate your repository with the munchausen-reinforcement-learning topic, visit your repo's landing page and select "manage topics."