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

https://github.com/rikluost/RL_DQN_Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/rishavb123/MineRL

Playing Atari with Deep Reinforcement Learning

https://github.com/riveSunder/carle

Exploration by Random Network Distillation

https://github.com/riveSunder/moving_in_morley

Exploration by Random Network Distillation

https://github.com/rle-foundation/rlexplore

Exploration by Random Network Distillation

https://github.com/rll/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rlworkgroup/garage

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/robfiras/ls-iq

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/robintyh1/icml2021-pengqlambda

Safe and Efficient Off-Policy Reinforcement Learning

https://github.com/roboticist-by-day/Rainbow_DQN

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/roop-pal/Meta-Learning-for-StarCraft-II-Minigames

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/rshnn/battleship

Proximal Policy Optimization Algorithms

https://github.com/russellmendonca/maesn_suite

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rwightman/pytorch-opensim-rl

Self-Imitation Learning

https://github.com/rybread1/DeepRlTrex

Deep Reinforcement Learning with Double Q-learning

https://github.com/rybread1/deep-rl-trex

Deep Reinforcement Learning with Double Q-learning

https://github.com/s-sd/task-amenability

Proximal Policy Optimization Algorithms

https://github.com/sagpant/TorchCraft

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

https://github.com/sagpant/ViZDoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/saha0073/Deep-Reinforcement-Learning-to-play-Cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/sainijagjit/A3C-Pytorch

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/samuelfneumann/GoAtar

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

https://github.com/sandeeprockstar/Pose_Estimation

An Empirical Model of Large-Batch Training

https://github.com/sannebh/riskaverse

Distributional Reinforcement Learning with Quantile Regression

https://github.com/sannebh/riskaverse_rl

Distributional Reinforcement Learning with Quantile Regression

https://github.com/sarahisyoung/rlpyt

Accelerated Methods for Deep Reinforcement Learning

https://github.com/saschaschramm/Pong

Proximal Policy Optimization Algorithms

https://github.com/sauxpa/ML_101

Temporal Regularization in Markov Decision Process

https://github.com/sc2crazy/StarCrackRL

Proximal Policy Optimization Algorithms

https://github.com/seacevedo/ReinforcementLearningProjects

Deep Reinforcement Learning with Double Q-learning

https://github.com/self-supervisor/how_to_stay_curious_while_avoiding_noisy_tvs

Large-Scale Study of Curiosity-Driven Learning

https://github.com/senya-ashukha/quantile-regression-dqn-pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/seolhokim/DistributedRL-Pytorch-Ray

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/seolhokim/SimpleDistributedRL

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/seungjaeryanlee/combined-experience-replay

A Deeper Look at Experience Replay

https://github.com/seungjaeryanlee/osim-rl-helper

Proximal Policy Optimization Algorithms

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