lxb678

lxb678's Stars

• jcwleo/curiosity-driven-exploration-pytorch

  Curiosity-driven Exploration by Self-supervised Prediction

  Language:Python13130

• jcwleo/mario_rl

  Language:Python6910

• chagmgang/pytorch_ppo_rl

  Pytorch implementation of intrinsic curiosity module with proximal policy optimization

  Language:Python5014

• deligentfool/policy_based_RL

  The implement of the policy gradient RL algorithm with pytorch

  Language:Python366

• adik993/ppo-pytorch

  Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)

  Language:Python12827

• MdMuzahid/RL_ICM

  Intrinsic Curiosity Module

  1

• rlcode/per

  Prioritized Experience Replay (PER) implementation in PyTorch

  Language:Python30076

• AmazingAng/WTF-DeepRL

  Deep RL algorithm in pytorch

  Language:Jupyter Notebook28254

• summerANDcode/SAC_PER

  Language:Python5

• XinJingHao/DRL-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)

  Language:Python1.1k135

• deligentfool/NFSP_lasertag

  The implement of Neural Fictitious Self Play with pytorch

  Language:Python1

• Srivatsan-Srinivasan/poker-NFSP

  Language:Python21

• NimaPublic/NFSP-CNN

  Language:Python1

• cptanalatriste/nfsp-playground

  Exploring Neural Fictitious Self-Play

  Language:Python2

• deligentfool/leduc_nfsp

  The implement of Neural Fictitious Self Play with pytorch

  Language:Python1

• dantodor/Neural-Ficititious-Self-Play-in-Imperfect-Information-Games

  This Project is based on Heinrich and Silvers Work "Neural Fictitious Self-Play in Imperfect Information Games". It includes the whole Game-Environment "Leduc Hold'em" which is inspired by the OpenAI Gym-Project. Furthermore it includes an NFSP Agent.

  Language:Python82

• arixlin/RL_NFSP

  Language:Python3

• thomasj02/nfsp-pytorch

  Neural Fictitious Self-Play in Pytorch

  Language:Python61

• liuxinyuanxy/NFSPwithHuman

  人类-智能体协同德州扑克平台及AI开发，拓展自http://turingai.ia.ac.cn/

  Language:Python7

• younggyoseo/pytorch-nfsp

  Implementation of Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (Heinrich and Silver, 2016)

  Language:Python4410

• PhDChe/Poker-1

  Various explorations into the game of Poker using MCTS, NFSP, and image-recognition/web-scraping

  123

• Lizhi-sjtu/DRL-code-pytorch

  Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

  Language:Python1k169

• BY571/SAC_discrete

  PyTorch implementation of the discrete Soft-Actor-Critic algorithm.

  Language:Python4112

• Meta-YZ/SAC-Discrete

  离散动作的SAC

  Language:Python4

• XinJingHao/SAC-Discrete-Pytorch

  A clean and robust Pytorch implementation of SAC on discrete action space

  Language:Python293

• p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

  PyTorch implementations of deep reinforcement learning algorithms and environments

  Language:Python5.5k1.2k

• toshikwa/sac-discrete.pytorch

  PyTorch implementation of SAC-Discrete.

  Language:Python27234

• yhl2333/p_guide-dr_missile

  gradesign

  Language:Python1

• yhl2333/gradu_design

  dqn-aircombat

  Language:Python41

• PKU-MARL/HARL

  Official implementation of HARL algorithms based on PyTorch.

  Language:Python44450