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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/iDataist/Tennis-With-Multi-Agent-Reinforcement

Prioritized Experience Replay

https://github.com/iankuoli/DFNet_TF2

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/ianlimle/ItsMeMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/icmlanon58443043/vizdoom

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

https://github.com/icmlanon58443043/vizdoomicmlanon

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

https://github.com/idsia/modern-srwm

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/idsia/neuraldiffeq-fwp

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/ifestus/rl

Trust Region Policy Optimization

https://github.com/iglu-contest/iglu-builder-baseline-dreamer

Mastering Atari with Discrete World Models

https://github.com/igoracmorais/inteligencia_artificial

Playing Atari with Deep Reinforcement Learning

https://github.com/ikostrikov/pytorch-a3c

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ikostrikov/pytorch-rl

Trust Region Policy Optimization

https://github.com/ikostrikov/pytorch-trpo

Trust Region Policy Optimization

https://github.com/inoryy/reaver

Deep Reinforcement Learning that Matters

https://github.com/instadeepai/flashbax

Prioritized Experience Replay

https://github.com/invictos/InsacarDQN

Playing Atari with Deep Reinforcement Learning

https://github.com/jachiam/cpo

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/jacobkooi/hadamax

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/jadag/DDQN_mario

Deep Reinforcement Learning with Double Q-learning

https://github.com/jakegrigsby/supersonic

Exploration by Random Network Distillation

https://github.com/janhuenermann/neurojs

Increasing the Action Gap: New Operators for Reinforcement Learning

https://github.com/jcoreyes/evolvingrl

Evolving Reinforcement Learning Algorithms

https://github.com/jcwleo/curiosity-driven-exploration-pytorch

Large-Scale Study of Curiosity-Driven Learning

https://github.com/jcwleo/random-network-distillation-pytorch

Exploration by Random Network Distillation

https://github.com/jeapostrophe/monaco

Monte Carlo Tree Search for Asymmetric Trees

https://github.com/jeffery1236/Atari_DoubleDeepQNetwork

Deep Reinforcement Learning with Double Q-learning

https://github.com/jerrodparker20/adaptive-transformers-in-rl

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

https://github.com/jezzarax/drlnd_p1_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/jhare96/reinforcement-learning

Exploration by Random Network Distillation

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

Deep Reinforcement Learning with Double Q-learning

https://github.com/jjgarau/DAGPolicyGradient

Evolving Reinforcement Learning Algorithms

https://github.com/johan-gras/MuZero

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

https://github.com/johannah/bootstrap_dqn

Deep Exploration via Bootstrapped DQN

https://github.com/jonaths/dqn-grid

Playing Atari with Deep Reinforcement Learning

https://github.com/jonaths/tf-dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/jongharyu/neural-svd

Spectral Inference Networks: Unifying Deep and Spectral Learning

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