Pinned

1. Soft-Actor-Critic-and-Extensions

   PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.

   Python 57 6

2. Upside-Down-Reinforcement-Learning

   Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.

   Jupyter Notebook 48 5

3. Deep-Reinforcement-Learning-Algorithm-Collection

   Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.

   Jupyter Notebook 19 3

4. DQN-Atari-Agents

   DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

   Jupyter Notebook 15 3

5. Munchausen-RL

   PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN

   Jupyter Notebook 9 1

6. IQN-and-Extensions

   PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER, Noisy layer, N-step bootstrapping, Dueling architecture and…

   Jupyter Notebook 11 3