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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/qiongwu86/ris-vec-marl

공식 코드

Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning

https://github.com/qiongwu86/semantic-aware-resource-management-for-c-v2x-platooning-via-multi-agent-reinforcement-learning

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Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning

https://github.com/qiushi-lin/sacha

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SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding

https://github.com/raaslab/rl_multi_agent

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Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring

https://github.com/rafaelmp2/causal-marl

공식 코드

Discovering Causality for Efficient Cooperation in Multi-Agent Environments

https://github.com/rafaelmp2/marl-indep-comm

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Fully Independent Communication in Multi-Agent Reinforcement Learning

https://github.com/raffaelegalliera/melissa

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Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning

https://github.com/rallen10/ergo_particle_gym

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Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning

https://github.com/rbokade/pytsc

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PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control

https://github.com/reinholdm/offline-pre-trained-multi-agent-decision-transformer

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Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks

https://github.com/renweiya/RFQ-RFAC

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Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning

https://github.com/replicable-marl/marllib

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MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

https://github.com/rhoowd/sched_net

공식 코드

Learning to Schedule Communication in Multi-agent Reinforcement Learning

https://github.com/ringbdstack/ace-hgnn

공식 코드

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

https://github.com/ringbdstack/sr-marl

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Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles

https://github.com/robosafe-lab/sim4ad

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HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation

https://github.com/roger-creus/centralized-control-lux

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Centralized control for multi-agent RL in a complex Real-Time-Strategy game

https://github.com/romanlee6/multi_LLM_comm

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Theory of Mind for Multi-Agent Collaboration via Large Language Models

https://github.com/rommeoijcai2019/rommeo

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A Regularized Opponent Model with Maximum Entropy Objective

https://github.com/rotskoff-group/marl-design

공식 코드

Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control

https://github.com/rtloftin/hi-c

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Uncoupled Learning of Differential Stackelberg Equilibria with Commitments

https://github.com/runzhestat/causalmarl

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A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets

https://github.com/s1s3r4/muav-mtt-mappo

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A MARL Based Multi-Target Tracking Algorithm Under Jamming Against Radar

https://github.com/saferl-lab/safe-marl-in-autonomous-driving

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Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving

https://github.com/saizhang0218/TMC

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Succinct and Robust Multi-Agent Communication With Temporal Message Control

https://github.com/saizhang0218/VBC

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Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

https://github.com/salesforce/rirl

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Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal

https://github.com/salesforce/strategically-robust-ai

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Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents

https://github.com/salesforce/warp-drive

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WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

https://github.com/samjia2000/HSP

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Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased

https://github.com/satchitchatterji/shieldedmarlthesis

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Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning

https://github.com/schroederdewitt/mackrl

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Multi-Agent Common Knowledge Reinforcement Learning

https://github.com/schroederdewitt/meme

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Communicating via Markov Decision Processes

https://github.com/schroederdewitt/multiagent_mujoco

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FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/sftekin/rl-focal

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Multi-Agent Reinforcement Learning with Focal Diversity Optimization

https://github.com/shanghai-digital-brain-laboratory/db-football

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An Empirical Study on Google Research Football Multi-agent Scenarios

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