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

https://github.com/JL321/Proximal-Policy-Optimization

Proximal Policy Optimization Algorithms

https://github.com/JackFurby/Breakout

Playing Atari with Deep Reinforcement Learning

https://github.com/JacobLaney/cgp-tetris

Evolving simple programs for playing Atari games

https://github.com/JayLohokare/banana-world-navigation

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/Joearrowsmith/hackcambridge_2020

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/JohanSamir/rainbow_extend

Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

https://github.com/JonasRSV/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/JonasRSV/DQNTensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/JonasRSV/PPO

Proximal Policy Optimization Algorithms

https://github.com/JuanCCS/muzero-jc

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

https://github.com/JulT1/RL_SS19

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/JuliaPOMDP/DeepQLearning.jl

Playing Atari with Deep Reinforcement Learning

https://github.com/JulienDesvergnes/human-reinforcement-learning

Deep reinforcement learning from human preferences

https://github.com/JustinStitt/acrobotDDQN

Deep Reinforcement Learning with Double Q-learning

https://github.com/Justkim/random-network-distillation-pytorch

Exploration by Random Network Distillation

https://github.com/Jzar/Space-Invaders-DQN

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/K-tang-mkv/baseRLAlgorithm

Playing Atari with Deep Reinforcement Learning

https://github.com/KAIST-AILab/deeprl_practice_colab

Prioritized Experience Replay

https://github.com/Kaixhin/ACER

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Kaixhin/EC

Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means

https://github.com/Kaixhin/NoisyNet-A3C

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Kaixhin/Rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/KarlXing/RL-Visual-Continuous-Control

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/KatyNTsachi/Hierarchical-RL

Playing Atari with Deep Reinforcement Learning

https://github.com/KavindaKottege/DeepQ-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/Kchu/DeepRL_CK

A Distributional Perspective on Reinforcement Learning

https://github.com/KelvinYang0320/deepbots-panda

Deep Reinforcement Learning with Double Q-learning

https://github.com/Khrylx/PyTorch-RL

Trust Region Policy Optimization

https://github.com/King-Of-Knights/overcoming-catastrophic

Overcoming catastrophic forgetting in neural networks

https://github.com/Kunal-Kumar-Sahoo/Deep-Reinforcement-Learning-with-Human-Preferences

Deep reinforcement learning from human preferences

https://github.com/KyleChen400/patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/LAS1520/Gato-A-Generalist-Agent

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Lamikins/public_exp

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/LeejwUniverse/RL_Exploration_Pytorch

Exploration by Random Network Distillation

https://github.com/Liberty3000/rl

Relational Deep Reinforcement Learning

https://github.com/LilTwo/DRL-using-PyTorch

Noisy Networks for Exploration

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