Modularized Implementation of Deep RL Algorithms in PyTorch
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Updated
Apr 16, 2024 - Python
Modularized Implementation of Deep RL Algorithms in PyTorch
Clean, Robust, and Unified PyTorch implementation of popular DRL Algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Prioritized Experience Replay (PER) implementation in PyTorch
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
Repository for codes of 'Deep Reinforcement Learning'
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.
A novel DDPG method with prioritized experience replay (IEEE SMC 2017)
强化学习算法库,包含了目前主流的强化学习算法(Value based and Policy based)的代码,代码都经过调试并可以运行
PyTorch implementation of various reinforcement learning algorithms
Prioritized Experience Replay implementation with proportional prioritization
Pytorch implementation of distributed deep reinforcement learning
Implementation of Deep Deterministic Policy Gradient (DDPG) with Prioritized Experience Replay (PER)
RLCodebase: PyTorch Codebase For Deep Reinforcement Learning Algorithms
Applying the DQN-Agent from keras-rl to Starcraft 2 Learning Environment and modding it to to use the Rainbow-DQN algorithms.
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.
Using N-step dueling DDQN with PER for playing Pacman game
Modular-HER is revised from OpenAI baselines and supports many improvements for Hindsight Experience Replay as modules.
Actor Prioritized Experience Replay
Lightweight deep RL Libraray for discrete control.
A Torch Based RL Framework for Rapid Prototyping of Research Papers
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