BY571
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.
Upside-Down-Reinforcement-Learning
Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.
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
Normalized-Advantage-Function-NAF-
PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method
CQL
PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.
Deep-Reinforcement-Learning-Algorithm-Collection
Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.
FQF-and-Extensions
PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueli...
Implicit-Q-Learning
PyTorch implementation of the implicit Q-learning algorithm (IQL)
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 p...
Munchausen-RL
PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN