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

https://github.com/RobotMobile/rl-paper-review

Playing Atari with Deep Reinforcement Learning

https://github.com/Roman-Kozachek/TradeBot

Deep Reinforcement Learning with Double Q-learning

https://github.com/SHRIVP/muzero

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

https://github.com/SPark9625/Large-Scale-Study-of-Curiosity-Driven-Learning

Large-Scale Study of Curiosity-Driven Learning

https://github.com/SPark9625/PyTorch-Proximal-Policy-Optimization

Proximal Policy Optimization Algorithms

https://github.com/SalvatoreCognetta/reasoning-agent-project

Proximal Policy Optimization Algorithms

https://github.com/SayhoKim/tetrisRL

Playing Atari with Deep Reinforcement Learning

https://github.com/SeungeonBaek/continuous-agents-test

Self-Imitation Learning

https://github.com/SeungeonBaek/discrete-agents-test

Distributional Reinforcement Learning with Quantile Regression

https://github.com/SforAiDl/vformer

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/ShangtongZhang/DistributedES

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Sheepsody/Batched-Impala-PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/ShibiHe/Q-Optimality-Tightening

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning_from_scratch

Playing Atari with Deep Reinforcement Learning

https://github.com/ShuhuaGao/gpFlappyBird

Evolving simple programs for playing Atari games

https://github.com/SimonRamstedt/ddpg

Prioritized Experience Replay

https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

Trust Region Policy Optimization

https://github.com/Stippler/cow-simulator

Proximal Policy Optimization Algorithms

https://github.com/Suryavf/SelfDrivingCar

Prioritized Experience Replay

https://github.com/TeamSAIDA/SAIDA

Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders

https://github.com/Teslatic/SC2-Freiburg

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/TestGenome/MSCS

MSC: A Dataset for Macro-Management in StarCraft II

https://github.com/TheFebrin/DeepRL-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/TonghanWang/DOP

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/TonghanWang/RODE

RODE: Learning Roles to Decompose Multi-Agent Tasks

https://github.com/TooTouch/3D_CoordConv_Segmentation

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/ToolManChang/DRLseminar_code_challenge

Soft Actor-Critic for Discrete Action Settings

https://github.com/TorchCraft/TorchCraft

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

https://github.com/UesugiErii/tf2-PPO-atari

Proximal Policy Optimization Algorithms

https://github.com/V0LsTeR/DQN_heap

Prioritized Experience Replay

https://github.com/V0LsTeR/dopamine_prioritized_buffer

Prioritized Experience Replay

https://github.com/VasaKiDD/TD3-deep-rl-research

Prioritized Experience Replay

https://github.com/VasaKiDD/end-to-end-drug-discovery

Fractal AI: A fragile theory of intelligence

https://github.com/VictorZuanazzi/Project_RL

Prioritized Experience Replay

https://github.com/Wadaboa/flatland-challenge

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

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