https://github.com/011235813/cm3
공식 코드CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Papers with Code 아카이브
Multi-agent Reinforcement Learning
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The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.
Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/011235813/hierarchical-marl
공식 코드Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery
https://github.com/011235813/marl-amr
공식 코드Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
https://github.com/AVSLab/bsk_rl
공식 코드Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey
https://github.com/Acciorocketships/pymarl2
공식 코드QGNN: Value Function Factorisation with Graph Neural Networks
https://github.com/AdelNabli/MCN
공식 코드Curriculum learning for multilevel budgeted combinatorial problems
https://github.com/AdrienLin1/MACDPP
공식 코드Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization
https://github.com/AnujMahajanOxf/MAVEN
공식 코드https://github.com/CANVOLCANO/DPMAC
공식 코드DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
https://github.com/CODE-SUBMIT/simulator2
공식 코드Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes
https://github.com/Detry322/DeepRole
공식 코드https://github.com/FXDevailly/IG-RL
공식 코드IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
https://github.com/Farama-Foundation/PettingZoo
공식 코드https://github.com/Future-Power-Networks/MAPDN
공식 코드Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
https://github.com/Haiyang-W/MAVN
공식 코드https://github.com/Harry67Hu/CORY
공식 코드https://github.com/HumanCompatibleAI/overcooked_ai
공식 코드ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination
https://github.com/IDSIA/marl
공식 코드Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
https://github.com/IouJenLiu/PIC
공식 코드PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
https://github.com/Jaroan/Fair-MARL
공식 코드Cooperation and Fairness in Multi-Agent Reinforcement Learning
https://github.com/JohannesAck/MATD3implementation
공식 코드Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
https://github.com/KafuuChikai/Dashing-for-the-Golden-Snitch-Multi-Drone-RL
공식 코드https://github.com/Lamperougeyxy/GHQ
공식 코드GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning
https://github.com/LearningToGatherWithoutCommunication/Ring
공식 코드https://github.com/LinShiqi047/PatchAutoAugment
공식 코드https://github.com/Liza-Tennant/modeling_moral_choice_dyadic
공식 코드Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
https://github.com/Nikunj-Gupta/HAMMER
공식 코드HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging
https://github.com/Nikunj-Gupta/dmcg-marl
공식 코드Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning
https://github.com/PKU-AI-Edge/I2C
공식 코드Learning Individually Inferred Communication for Multi-Agent Cooperation
https://github.com/PPSantos/hybrid-marl
공식 코드Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
https://github.com/PaLeroy/QVMix
공식 코드https://github.com/Passerby/right-of-way-rl
공식 코드https://github.com/RS2002/OSPO
공식 코드https://github.com/Rainlabuw/rl-enabled-distributed-assignment
공식 코드Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems
https://github.com/RingBDStack/AR-DBSCAN
공식 코드Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning
https://github.com/SCP-CN-001/trajectory_dataset_support
공식 코드Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.