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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/Codernauti/Exploration-of-DQN-in-CartPole-Environment

Deep Reinforcement Learning with Double Q-learning

https://github.com/CoffeeddCat/Multiagent_chainMDP

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

https://github.com/ContinualAI/avalanche

Overcoming catastrophic forgetting in neural networks

https://github.com/Curt-Park/rainbow-is-all-you-need

Playing Atari with Deep Reinforcement Learning

https://github.com/DHDev0/Muzero

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

https://github.com/DHDev0/Muzero-unplugged

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/DLR-RM/stable-baselines3

Playing Atari with Deep Reinforcement Learning

https://github.com/Dam930/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/Damcy/prioritized-experience-replay

Prioritized Experience Replay

https://github.com/DanielLSM/safe-rl-tutorial

Safe and Efficient Off-Policy Reinforcement Learning

https://github.com/DanielTakeshi/dqn

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

https://github.com/Dascka/dl_q-learning_comparator

Deep Reinforcement Learning that Matters

https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Denbergvanthijs/imbDRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/Dorozhko-Anton/go-explore

Go-Explore: a New Approach for Hard-Exploration Problems

https://github.com/DuaneNielsen/rnd

Exploration by Random Network Distillation

https://github.com/EmptyJackson/groove

Discovering Reinforcement Learning Algorithms

https://github.com/Epsilon10/MARL

Soft Actor-Critic for Discrete Action Settings

https://github.com/FaboNo/DRLND

Deep Reinforcement Learning with Double Q-learning

https://github.com/FauzaanQureshi/deep-Q-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/FlixCoder/rust-es-optimizer

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/FragileTech/FractalAI

Fractal AI: A fragile theory of intelligence

https://github.com/GabrielAlacchi/atari-ari-extensions

Unsupervised State Representation Learning in Atari

https://github.com/GaoFangshu/ELF-example

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

https://github.com/Gary-Shi/Tank

Playing Atari with Deep Reinforcement Learning

https://github.com/GoingMyWay/dopamine_reward_decomposition

Prioritized Experience Replay

https://github.com/GuanSuns/Simple-Human-in-the-Loop-ML-Interface

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

https://github.com/Guillaume-Cr/lunar_lander_per

Prioritized Experience Replay

https://github.com/Guillemdb/FractalAI

Fractal AI: A fragile theory of intelligence

https://github.com/Guillemdb/fragile

Fractal AI: A fragile theory of intelligence

https://github.com/Howuhh/prioritized_experience_replay

Prioritized Experience Replay

https://github.com/HussonnoisMaxence/RL_Algorithms

Deep Reinforcement Learning with Double Q-learning

https://github.com/HzcIrving/DecisionTransformer_StepbyStep

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/IDSIA/lmtool-fwms

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/IDSIA/lmtool-fwp

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

https://github.com/InSpaceAI/RL-Zoo

Playing Atari with Deep Reinforcement Learning

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