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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/uber-research/ape-x

Distributed Prioritized Experience Replay

https://github.com/ugo-nama-kun/DQN-chainer

Playing Atari with Deep Reinforcement Learning

https://github.com/ulstu/ml

Deep Attention Recurrent Q-Network

https://github.com/ulstu/robotics_ml

Deep Attention Recurrent Q-Network

https://github.com/uoe-agents/derl

#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

https://github.com/urw7rs/spiralpp

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

https://github.com/utarumo/RL_implementation

Deep Reinforcement Learning with Double Q-learning

https://github.com/uvipen/Super-mario-bros-A3C-pytorch

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/vcharvet/project-rl

Deep reinforcement learning from human preferences

https://github.com/vdean/audio-curiosity

Large-Scale Study of Curiosity-Driven Learning

https://github.com/vicsesi/PyTorch-STN

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/vik-goel/MOREL

Unsupervised Video Object Segmentation for Deep Reinforcement Learning

https://github.com/villinvic/Georges

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

https://github.com/vincentpalma/DQN-for-CaRL

Playing Atari with Deep Reinforcement Learning

https://github.com/vkumaresan/Rotational-CoordConv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/vladfi1/universe-starter-agent

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/vsquareg/RL_ERA

Playing Atari with Deep Reinforcement Learning

https://github.com/vub-ai-lab/qwr

Q-Value Weighted Regression: Reinforcement Learning with Limited Data

https://github.com/vwxyzjn/cleanrl

Distributed Prioritized Experience Replay

https://github.com/wabbajack1/tapd

Progress & Compress: A scalable framework for continual learning

https://github.com/walsvid/CoordConv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/wangyy161/DDPG_CNN_Pendulum_practice

Trust Region Policy Optimization

https://github.com/wannabeOG/MAS-PyTorch

Overcoming catastrophic forgetting in neural networks

https://github.com/windstrip/DeepMind-StreetLearn

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

https://github.com/wjmacro/continualmt

Overcoming catastrophic forgetting in neural networks

https://github.com/wmol4/Pytorch_DDQN_Unity_Navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/woans0104/project_review

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/woans0104/sk_project

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/wtingda/DeepRLBreakout

Deep Reinforcement Learning with Double Q-learning

https://github.com/wxj77/TransferReinforcementLearning

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/wyatuestc/overcoming-catastrophic

Overcoming catastrophic forgetting in neural networks

https://github.com/wyndwarrior/imitation_from_observation

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/xValentim/Steering_Behaviors_with_pygame

Playing Atari with Deep Reinforcement Learning

https://github.com/xavierchanglingli/regularized-optimal-experience-replay

Large Batch Experience Replay

https://github.com/xduan7/hat-cl

Overcoming catastrophic forgetting in neural networks

https://github.com/xgfelicia/Reinforcement-Learning

Deep Reinforcement Learning with Double Q-learning

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