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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/2024-MindSpore-1/Code2/tree/main/model-1/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/4rChon/NL-FuN

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/7starsea/Prioritized-Experience-Replay

Prioritized Experience Replay

https://github.com/ACampero/dopamine

Prioritized Experience Replay

https://github.com/AI-RG/rl-experiments

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Abdelhamid-bouzid/Distributional-RL

A Distributional Perspective on Reinforcement Learning

https://github.com/Adeikalam/Go-Explore

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

https://github.com/Adrelf/DRL-navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/Amadeus979/decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/AndrewJWashington/protodriver

Playing Atari with Deep Reinforcement Learning

https://github.com/Angeliki-Panoulia/CallForCode

Scalable agent alignment via reward modeling: a research direction

https://github.com/AnnaNikitaRL/EVA

Fast deep reinforcement learning using online adjustments from the past

https://github.com/AnshMittal1811/AugmentedRandomSearch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Anshu1245/RL-CourseProject

Playing Atari with Deep Reinforcement Learning

https://github.com/Arrabonae/openai_DDDQN

Prioritized Experience Replay

https://github.com/AsWali/MineAgentFinal

MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

https://github.com/AurelianTactics/dqnclipped_dqnreg_prelim_implementation

Evolving Reinforcement Learning Algorithms

https://github.com/AvivNavon/AuxiLearn

Adapting Auxiliary Losses Using Gradient Similarity

https://github.com/BH4/Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/BY571/DQN-Atari-Agents

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/BY571/FQF-and-Extensions

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

https://github.com/BY571/IQN

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/BY571/Munchausen-RL

Munchausen Reinforcement Learning

https://github.com/BY571/SAC_discrete

Soft Actor-Critic for Discrete Action Settings

https://github.com/Bam4d/GVGAI_GYM

Deep Reinforcement Learning for General Video Game AI

https://github.com/BigHeaded2003/ConQUR-Mitigating-Delusional-Bias-in-Deep-Q-Learning

ConQUR: Mitigating Delusional Bias in Deep Q-learning

https://github.com/Bigpig4396/PyTorch-Soft-Actor-Critic-SAC

Soft Actor-Critic for Discrete Action Settings

https://github.com/Brandon-Rozek/DeepRL

Prioritized Experience Replay

https://github.com/CSCI4850/S20-team3-project

Prioritized Experience Replay

https://github.com/CVC-Lab/SAC-for-H-Bond-Learning

Mastering Atari with Discrete World Models

https://github.com/CankayaUniversity/ceng-407-408-License-Plate-Recognition-Using-Deep-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/CharlotteMorrison/Baxter-Research

Prioritized Experience Replay

https://github.com/CharlotteMorrison/Baxter-VREP

Prioritized Experience Replay

https://github.com/CharlotteMorrison/Baxter-VREP-Version-2

Prioritized Experience Replay

https://github.com/Clement-Hui/Q-Learning

Prioritized Experience Replay

https://github.com/Coac/never-give-up

Never Give Up: Learning Directed Exploration Strategies

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