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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/link-kut/rl

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

https://github.com/lireer/ricochet-robot-solver

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/liuyuezhang/pyrl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/londoed/Gorila

Massively Parallel Methods for Deep Reinforcement Learning

https://github.com/londoed/Kortex

Massively Parallel Methods for Deep Reinforcement Learning

https://github.com/lvyufeng/DQN-MindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/lyebi/Test

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

https://github.com/madeleinesnyder/Subbat

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

https://github.com/magnusja/ppo

Trust Region Policy Optimization

https://github.com/manvibharat/Stock-price-pridiction-using-Deep-reienforcement-learning

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/markusdutschke/yahtzee

Playing Atari with Deep Reinforcement Learning

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

Playing Atari with Deep Reinforcement Learning

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/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

Accelerated Methods for Deep Reinforcement Learning

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

Exploration by Random Network Distillation

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

Deep Reinforcement Learning with Double Q-learning

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/miyosuda/async_deep_reinforce

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/mklissa/dceo

Eigenoption Discovery through the Deep Successor Representation

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