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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Wentworth1996/Summer_Intern_Progress

Playing Atari with Deep Reinforcement Learning

https://github.com/Wizaron/coord-conv-pytorch

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/YHL04/agent57

Never Give Up: Learning Directed Exploration Strategies

https://github.com/YanSong97/Master-thesis

Noisy Networks for Exploration

https://github.com/YuansongFeng/MadMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/YuhangSong/Mega-Reward

Deep Reinforcement Learning that Matters

https://github.com/YuriCat/MuZeroJupyterExample

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/YuriCat/MuesliJupyterExample

Muesli: Combining Improvements in Policy Optimization

https://github.com/ZachisGit/LearningFromHumanPreferences

Deep reinforcement learning from human preferences

https://github.com/ZainRaza14/deepRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/Zartris/TD3_continuous_control

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Zeta36/muzero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/ZiyiLiubird/DeepMARL-PyTorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ZiyuanMa/reversi

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/ZwX1616/mxnet-CoordConv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/aabbeell/reinforcementLearning.a2c.gym

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/abryeemessi/Wednesday

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/ac-93/soft-actor-critic

Soft Actor-Critic for Discrete Action Settings

https://github.com/adamos581/ppo-keras-football

Proximal Policy Optimization Algorithms

https://github.com/adik993/ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/adityabingi/Dreamer

Mastering Atari with Discrete World Models

https://github.com/ailab-pku/rl-framework

Trust Region Policy Optimization

https://github.com/ailabdsunipi/pymarlzooplus

The StarCraft Multi-Agent Challenge

https://github.com/aimagelab/mammoth

Overcoming catastrophic forgetting in neural networks

https://github.com/akterskii/rlpyt

Accelerated Methods for Deep Reinforcement Learning

https://github.com/alecfilios/Training-Intelligent-game-Agents-through-Competitive-Reinforcement-Learning

Proximal Policy Optimization Algorithms

https://github.com/alessandrositta/Flatland_challenge

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/alexbaumi/PPO-Algorithm

Proximal Policy Optimization Algorithms

https://github.com/alexbaumi/PPO-Algorithms

Proximal Policy Optimization Algorithms

https://github.com/alexmlamb/blocks_rl_gru_setup

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/alexsax/testing_rlpyt

Accelerated Methods for Deep Reinforcement Learning

https://github.com/alfredvc/paac

Playing Atari with Deep Reinforcement Learning

https://github.com/alhabk/SGEE--pytorch

Self-Imitation Learning

https://github.com/alirezakazemipour/ppo-rnd

Proximal Policy Optimization Algorithms

https://github.com/alisidd/Evolution-Strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/aluscher/torchbeastpopart

Multi-task Deep Reinforcement Learning with PopArt

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.