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 Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
Prioritized Experience Replay (PER) implementation in PyTorch
PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.
Repository for codes of 'Deep Reinforcement Learning'
Pytorch implementation of distributed deep reinforcement learning
Prioritized Experience Replay implementation with proportional prioritization
A Torch Based RL Framework for Rapid Prototyping of Research Papers
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
A novel DDPG method with prioritized experience replay (IEEE SMC 2017)
Implementation of Deep Deterministic Policy Gradient (DDPG) with Prioritized Experience Replay (PER)
RLCodebase: PyTorch Codebase For Deep Reinforcement Learning Algorithms
Using N-step dueling DDQN with PER for playing Pacman game
PyTorch implementation of various reinforcement learning 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.
Modular-HER is revised from OpenAI baselines and supports many improvements for Hindsight Experience Replay as modules.
Actor Prioritized Experience Replay
Applying the DQN-Agent from keras-rl to Starcraft 2 Learning Environment and modding it to to use the Rainbow-DQN algorithms.
This repository implements the use of AI for robot tasks.
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