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

https://github.com/fdcl-gwu/gym-rotor

Proximal Policy Optimization Algorithms

https://github.com/felixriese/CNN-SoilTextureClassification

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/fengsterooni/dql

Deep Reinforcement Learning with Double Q-learning

https://github.com/fiberleif/evolution-strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/filippogiruzzi/deep_q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/fionisarnen/Lifelong-Learning-churn-prediction-torch

Overcoming catastrophic forgetting in neural networks

https://github.com/floringogianu/atari-agents

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

https://github.com/fmorenovr/Attention_Mechanism

Performing Deep Recurrent Double Q-Learning for Atari Games

https://github.com/foersterrobert/MuZero

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

https://github.com/fomorians/forward-models

Recurrent Environment Simulators

https://github.com/forevergj/multithreading_ppo_RND

Exploration by Random Network Distillation

https://github.com/fuyuan-li/tensorflow-RIMs

Recurrent Independent Mechanisms

https://github.com/g-u-n/pycil

Overcoming catastrophic forgetting in neural networks

https://github.com/gaetanserre/l2rpn-2022_ppo-baseline

Proximal Policy Optimization Algorithms

https://github.com/garymm/earl/tree/master/earl/agents/r2d2

https://github.com/geeky-wizard/Atari-Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/geohot/twitchcoq

Value Prediction Network

https://github.com/georgkruse/cleanqrl

Proximal Policy Optimization Algorithms

https://github.com/ghostFaceKillah/expert

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/gijskoning/ReproducingCURL

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/gingkg/smac

Counterfactual Multi-Agent Policy Gradients

https://github.com/glenn89/FederatedRL

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

https://github.com/gmoss1/Kaggle-Halite-IV-RL

Proximal Policy Optimization Algorithms

https://github.com/gnouhp/PyTorch-CoordConv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/goncharom/PPOv1

Proximal Policy Optimization Algorithms

https://github.com/goodbyeearth/myVBC

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

https://github.com/google-deepmind/csil

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/google-deepmind/pysc2

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/google-deepmind/scalable_agent

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

https://github.com/google-deepmind/streetlearn

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

https://github.com/google-research/valan

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

https://github.com/gordicaleksa/pytorch-learn-reinforcement-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/gouxiangchen/dueling-DQN-pytorch

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/gowriaddepalli/ML_DL_Research_collab_base

Deep Reinforcement Learning framework for Autonomous Driving

https://github.com/grananqvist/reinforcement-learning-super-mario-A3C

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/grass123-hub/DQN

Playing Atari with Deep Reinforcement Learning

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