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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/pskugit/custom-conv2d

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/puppetect/TradingBot-tensorflow

Deep Reinforcement Learning with Double Q-learning

https://github.com/pwc-1/Paper-8/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/a2c

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/qgallouedec/deep_rl

A Distributional Perspective on Reinforcement Learning

https://github.com/qiankun214/DQN-FlappyBird-python3

Playing Atari with Deep Reinforcement Learning

https://github.com/qihongl/demo-advantage-actor-critic

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/qihongl/dlstm-demo

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/rasmusbergpalm/evostrat

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/ray-project/ray/tree/master/rllib

Playing Atari with Deep Reinforcement Learning

https://github.com/rddy/ReQueST

Reward learning from human preferences and demonstrations in Atari

https://github.com/rejuvyesh/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rgreenblatt/ai_alignment_readings

Scalable agent alignment via reward modeling: a research direction

https://github.com/richardrl/cartpole-request-for-research

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rikluost/RL_DQN_Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/rishavb123/MineRL

Playing Atari with Deep Reinforcement Learning

https://github.com/riveSunder/carle

Exploration by Random Network Distillation

https://github.com/riveSunder/moving_in_morley

Exploration by Random Network Distillation

https://github.com/rle-foundation/rlexplore

Exploration by Random Network Distillation

https://github.com/rll/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rlworkgroup/garage

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/robfiras/ls-iq

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/robintyh1/icml2021-pengqlambda

Safe and Efficient Off-Policy Reinforcement Learning

https://github.com/roboticist-by-day/Rainbow_DQN

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/roop-pal/Meta-Learning-for-StarCraft-II-Minigames

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/russellmendonca/maesn_suite

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rwightman/pytorch-opensim-rl

Self-Imitation Learning

https://github.com/rybread1/DeepRlTrex

Deep Reinforcement Learning with Double Q-learning

https://github.com/rybread1/deep-rl-trex

Deep Reinforcement Learning with Double Q-learning

https://github.com/sagpant/ViZDoom

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

https://github.com/saha0073/Deep-Reinforcement-Learning-to-play-Cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/sainijagjit/A3C-Pytorch

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/samuelfneumann/GoAtar

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

https://github.com/sannebh/riskaverse

Distributional Reinforcement Learning with Quantile Regression

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