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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MAS-anony/ASN
공식 코드Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
https://github.com/MichaelAllen1966/qambo
공식 코드https://github.com/MineDojo/MineDojo
공식 코드MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
https://github.com/MishaLaskin/curl
공식 코드CURL: Contrastive Unsupervised Representations for Reinforcement Learning
https://github.com/MouseHu/GEM
공식 코드Generalizable Episodic Memory for Deep Reinforcement Learning
https://github.com/NUS-LID/RENAULT
공식 코드Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning
https://github.com/NVLABs/cule
공식 코드Accelerating Reinforcement Learning through GPU Atari Emulation
https://github.com/NelsonAU/ale_countstates
공식 코드https://github.com/NeteaseFuxiRL/action-balance-exploration
공식 코드https://github.com/NinaMaz/NAS_RL_torch
공식 코드Optimizing the Neural Architecture of Reinforcement Learning Agents
https://github.com/OscarHuangWind/Preference-Guided-DQN-Atari
공식 코드Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration
https://github.com/PaLeroy/QVMix
공식 코드https://github.com/PatrykChrabaszcz/Canonical_ES_Atari
공식 코드Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
https://github.com/RLforlife/VER
공식 코드Revisiting Prioritized Experience Replay: A Value Perspective
https://github.com/SamanKhamesian/Hybrid-Self-Attention-NEAT
공식 코드Hybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm
https://github.com/Spiegeldondi/A-Hybrid-Quantum-Classical-Framework-for-Reinforcement-Learning-of-Atari-Games
공식 코드A quantum-classical reinforcement learning model to play Atari games
https://github.com/TeaPearce/Counter-Strike_Behavioural_Cloning
공식 코드Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
https://github.com/TeamSAIDA/DefogGAN
공식 코드DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
https://github.com/Tencent/TStarBots
공식 코드TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
https://github.com/TheHedgeify/DagstuhlGAN
공식 코드Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
https://github.com/TonghanWang/NDQ
공식 코드Learning Nearly Decomposable Value Functions Via Communication Minimization
https://github.com/TorchCraft/StarData
공식 코드https://github.com/TorchCraft/TorchCraftAI
공식 코드https://github.com/Unity-Technologies/obstacle-tower-env
공식 코드Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
https://github.com/Usama1002/EBE-Exploration
공식 코드https://github.com/Wei9711/GACG
공식 코드Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
https://github.com/Wei9711/LTSCG
공식 코드Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
https://github.com/WendyShang/dqn_zoo
공식 코드https://github.com/WendyShang/flare
공식 코드https://github.com/WolfLo/optimist
공식 코드Conservative Optimistic Policy Optimization via Multiple Importance Sampling
https://github.com/XiaoxiaoGuo/tensor4rl
공식 코드https://github.com/YuhangSong/GTN
공식 코드Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning
https://github.com/Yusufma03/CVRL
공식 코드Contrastive Variational Reinforcement Learning for Complex Observations
https://github.com/Yusufma03/DPFRL
공식 코드Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
https://github.com/YyzHarry/SV-RL
공식 코드Harnessing Structures for Value-Based Planning and Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.