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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/nice-hku/cl2marl-smac

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Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement Learning

https://github.com/nicosquare/rl-energy-management

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MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids

https://github.com/nikunj-gupta/conformal-agent-modelling

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CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning

https://github.com/nkai-decision-team/llm-pysc2

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LLM-PySC2: Starcraft II learning environment for Large Language Models

https://github.com/noahadhikari/pettingzoo-chinese-checkers

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Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement Learning

https://github.com/nrel/powergridworld

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PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

https://github.com/nsidn98/informarl

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Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

https://github.com/nudt-bi-marl/pmat

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PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning

https://github.com/openai/multi-agent-emergence-environments

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Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences

https://github.com/openai/multiagent-particle-envs

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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/opendilab/ace

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ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency

https://github.com/orenneumann/alphazero-scaling-laws

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Scaling Laws for a Multi-Agent Reinforcement Learning Model

https://github.com/osilab-kaist/smac_exp

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The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions

https://github.com/oxwhirl/pymarl

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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/smac

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The StarCraft Multi-Agent Challenge

https://github.com/oxwhirl/smacv2

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SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/wqmix

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Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/paperflight/Fed-MF-MAL

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Scalable Multi-agent Reinforcement Learning Algorithm for Wireless Networks

https://github.com/philip-jordan/iprox-cmpg

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Independent Learning in Constrained Markov Potential Games

https://github.com/pku-ai-edge/roadnetsz

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Learning to Share in Multi-Agent Reinforcement Learning

https://github.com/pku-marl/harl

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Heterogeneous-Agent Reinforcement Learning

https://github.com/pku-marl/multi-agent-transformer

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Multi-Agent Reinforcement Learning is a Sequence Modeling Problem

https://github.com/pku-rl/fop-dmac-macpf

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More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization

https://github.com/pra1trivedi/maccm

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Multi-Agent Congestion Cost Minimization With Linear Function Approximations

https://github.com/pranabiitp/fedmrl

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FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging

https://github.com/proroklab/adversarial_comms

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The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning

https://github.com/proroklab/controllingbehavioraldiversity

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Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning

https://github.com/proroklab/hetgppo

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Heterogeneous Multi-Robot Reinforcement Learning

https://github.com/proroklab/task-agnostic-comms

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Generalising Multi-Agent Cooperation through Task-Agnostic Communication

https://github.com/proroklab/vectorizedmultiagentsimulator

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VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning

https://github.com/puyuan1996/MARL

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Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

https://github.com/qeneb/ss-marl

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Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System

https://github.com/qian18long/epciclr2020

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Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

https://github.com/qihao067/poseexaminer

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PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation

https://github.com/qiongwu86/digital-twin-vehicular-edge-computing-network_task-offloading-and-resource-allocation

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Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation

https://github.com/qiongwu86/optimizing-aoi-in-vec-with-federated-graph-neural-network-multi-agent-reinforcement-learning

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Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning

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