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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/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/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/glenn89/FederatedRL

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

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

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

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

IQ-Learn: Inverse soft-Q Learning for Imitation

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

https://github.com/guillaumeboniface/bananaland

Deep Reinforcement Learning with Double Q-learning

https://github.com/gungui98/deeprl-a3c-ai2thor

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/gznyyb/deep_reinforcement_learning_Pong

Deep Reinforcement Learning with Double Q-learning

https://github.com/haje01/distper

Distributed Prioritized Experience Replay

https://github.com/haje01/impala

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

https://github.com/hamishs/JAX-RL

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/han-won/PlayingAtariWithMindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/harruff/Senior_Project_Repository

Trust Region Policy Optimization

https://github.com/harvitronix/reinforcement-learning-car

Playing Atari with Deep Reinforcement Learning

https://github.com/hegde95/ViZDoom_with_Sound

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

https://github.com/heiner/scalable_agent

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

https://github.com/hemilpanchiwala/Dueling-Network-Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Dueling_Network_Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hill-a/stable-baselines

Playing Atari with Deep Reinforcement Learning

https://github.com/htdt/rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/huihanl/rl_unplugged

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

https://github.com/hulanwin/A3C-DRL

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/hw9603/DQfD-PyTorch

Noisy Networks for Exploration

https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient

Prioritized Experience Replay

https://github.com/iDataist/Navigation-with-Deep-Q-Network

Prioritized Experience Replay

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