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Atari Games

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하위 태스크: 1
평가표: 65
보존된 평가 기록: 2,519

The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

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

https://github.com/joshiatul/game_playing

Playing Atari with Deep Reinforcement Learning

https://github.com/jrobine/actor-critic

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

https://github.com/jsztompka/DuelDQN

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/justinmaojones/starr

Prioritized Experience Replay

https://github.com/jvoynow/DQN-analysis-with-2048

Deep Reinforcement Learning with Double Q-learning

https://github.com/k-lombard/CS4641_Project

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

https://github.com/k-lombard/Deep-Learning-Chess-AI

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

https://github.com/kaesve/muzero

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

https://github.com/kaichiuwong/rlhps

Deep reinforcement learning from human preferences

https://github.com/kamsyn95/CL_DNN

Overcoming catastrophic forgetting in neural networks

https://github.com/kavosh8/MAC

Mean Actor Critic

https://github.com/kayuksel/pytorch-ars

Prioritized Experience Replay

https://github.com/keep9oing/DRQN-Pytorch-CartPole-v1

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/keraJLi/rejax

Soft Actor-Critic for Discrete Action Settings

https://github.com/kevslinger/dtqn

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/khanhptnk/bandit-nmt

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/kmdanielduan/DQN_Family_PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/kngwyu/intrinsic-rewards

Exploration by Random Network Distillation

https://github.com/kochlisGit/autonomous-vehicles-agent

Deep Reinforcement Learning with Double Q-learning

https://github.com/komejisatori/ReinforcementCar

Playing Atari with Deep Reinforcement Learning

https://github.com/koulanurag/muzero-pytorch

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

https://github.com/kshitij-ingale/Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ktkachuk/Atari-with-Q-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ku2482/fqf-iqn-qrdqn.pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/rljax

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/rltorch

Distributed Prioritized Experience Replay

https://github.com/ku2482/sac-discrete.pytorch

Prioritized Experience Replay

https://github.com/ku2482/soft-actor-critic.pytorch

Prioritized Experience Replay

https://github.com/la3lma/Chez

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/la3lma/chezjulia

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Playing Atari with Deep Reinforcement Learning

https://github.com/lbenbaccar/Deep-Reinforcement-Learning-with-Double-Q-Learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/lcswillems/torch-ac

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/learningandplanningICLR/learningandplanning

Uncertainty-sensitive Learning and Planning with Ensembles

https://github.com/lgerrets/rl18-curiosity

Exploration by Random Network Distillation

https://github.com/lingweizhu/Pytorch-MunchausenActorCritic

Soft Actor-Critic for Discrete Action Settings

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