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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/hyunghona/emu

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Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning

https://github.com/iacomlab/marl-patrol-routing

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Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through Multi-Agent Reinforcement Learning

https://github.com/ianRDavies/LeMOL

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Learning to Model Opponent Learning

https://github.com/ifpen/wfcrl-env

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WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control

https://github.com/illidanlab/Simulator

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Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management

https://github.com/ingyn/macsrl

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Logic-based Reward Shaping for Multi-Agent Reinforcement Learning

https://github.com/instadeepai/EGTA-NMARL

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A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning

https://github.com/instadeepai/Mava

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Mava: a research library for distributed multi-agent reinforcement learning in JAX

https://github.com/instadeepai/og-marl

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Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning

https://github.com/intelligent-environments-lab/CityLearn

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CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management

https://github.com/ispc-lab/marl4ad

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Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey

https://github.com/itstyren/InteractionMARL-Coop

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Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning

https://github.com/j3soon/dfac

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DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

https://github.com/j3soon/dfac-extended

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A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

https://github.com/jaggbow/saf

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Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

https://github.com/jahirsadik/cohet-implementation

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Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic Rewards

https://github.com/jbr-ai-labs/mamba

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Scalable Multi-Agent Model-Based Reinforcement Learning

https://github.com/jeffkinnison/shadho

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HomOpt: A Homotopy-Based Hyperparameter Optimization Method

https://github.com/jermcmahan/viser

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VISER: A Tractable Solution Concept for Games with Information Asymmetry

https://github.com/jf-hu/dre-marl

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Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning

https://github.com/jiechuanjiang/eoi_on_smac

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The Emergence of Individuality

https://github.com/jiechuanjiang/eoi_pymarl

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The Emergence of Individuality

https://github.com/jingdic/rgmcomm

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RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning

https://github.com/jiwonjeon9603/maser

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MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

https://github.com/jjbong/strangeness_exploration

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Strangeness-driven Exploration in Multi-Agent Reinforcement Learning

https://github.com/johannestreutlein/op-tie-breaking

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A New Formalism, Method and Open Issues for Zero-Shot Coordination

https://github.com/johannlussange/symba_crypto

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Modelling crypto markets by multi-agent reinforcement learning

https://github.com/johnMinelli/CoordinatedQMIX

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CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making

https://github.com/jon17591/multi-lingual-agents

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Multi-lingual agents through multi-headed neural networks

https://github.com/jpmorganchase/Phantom

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Phantom -- A RL-driven multi-agent framework to model complex systems

https://github.com/jshe/agent-time-attention

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Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning

https://github.com/jugg1er/air

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AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning

https://github.com/juhyeonkim95/TaxiSimulatorOnGraph

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Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network

https://github.com/julien6/moise-marl

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An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning

https://github.com/junaiddk/transmix

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Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft

https://github.com/jw3il/graph-marl

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Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing

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