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

https://github.com/marload/DistRL-TensorFlow2

A Distributional Perspective on Reinforcement Learning

https://github.com/marload/deep-rl-tf2

Playing Atari with Deep Reinforcement Learning

https://github.com/marload/dist-rl-tf2

A Distributional Perspective on Reinforcement Learning

https://github.com/marusan03/GAN-RL

Surprising Negative Results for Generative Adversarial Tree Search

https://github.com/matteokarldonati/Counterfactual-Multi-Agent-Policy-Gradients

Counterfactual Multi-Agent Policy Gradients

https://github.com/matthewsparr/Deep-Zork

Deep Reinforcement Learning with Double Q-learning

https://github.com/mavischer/DRRL

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/mengli11235/dim-ua

Unsupervised State Representation Learning in Atari

https://github.com/mfregeau/DeepLearning

Playing Atari with Deep Reinforcement Learning

https://github.com/mgbellemare/Arcade-Learning-Environment

The Arcade Learning Environment: An Evaluation Platform for General Agents

https://github.com/michael-snower/ppo

Proximal Policy Optimization Algorithms

https://github.com/michaelnny/deep_rl_zoo

Playing Atari with Deep Reinforcement Learning

https://github.com/michalnand/reinforcement_learning

Exploration by Random Network Distillation

https://github.com/microsoft/FQF

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

https://github.com/microsoft/Mask-based-Latent-Reconstruction

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/microsoft/med-deadend

Deep Reinforcement Learning with Double Q-learning

https://github.com/microsoft/strategically_efficient_rl

Proximal Policy Optimization Algorithms

https://github.com/mightypirate1/DRL-Tetris

Deep Reinforcement Learning with Double Q-learning

https://github.com/mightypirate1/PPO_homebrew

Proximal Policy Optimization Algorithms

https://github.com/mindspore-ai/contrib/blob/master/application/EWC-Overcoming-catastrophic-forgetting/demo.ipynb

Overcoming catastrophic forgetting in neural networks

https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/mindspore-courses/Rainbow-MindSpore

Deep Reinforcement Learning with Double Q-learning

https://github.com/mindspore-courses/Rainbow-MindSpore/blob/main/01.dqn.ipynb

https://github.com/mingyip/Rainbow-Reinforcement-Learning-For-Chess-Games

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/miroblog/tf_deep_rl_trader

Proximal Policy Optimization Algorithms

https://github.com/mit-realm/neuriss

Proximal Policy Optimization Algorithms

https://github.com/miyosuda/async_deep_reinforce

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/mklissa/dceo

Eigenoption Discovery through the Deep Successor Representation

https://github.com/mkocabas/CoordConv-pytorch

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/ml-jku/l2m

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/mmasana/FACIL

Overcoming catastrophic forgetting in neural networks

https://github.com/mng827/curve-gcn-cardiac-mr

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/moduIo/Deep-Q-network

Deep Reinforcement Learning with Double Q-learning

https://github.com/mohit8935/Deep-Q-Learning-Paper

Deep Reinforcement Learning with Double Q-learning

https://github.com/mohith-sakthivel/rainbow_dqn

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/molomono/CartPole_Optimized_DDQN

Deep Reinforcement Learning with Double Q-learning

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