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Multi-agent Reinforcement Learning

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

Papers

Showing 5175 of 1718 papers

TitleStatusHype
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial ExplorationCode1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
Trajectory-Class-Aware Multi-Agent Reinforcement LearningCode1
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement LearningCode1
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehiclesCode1
Training Language Models for Social Deduction with Multi-Agent Reinforcement LearningCode1
An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative TasksCode1
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm ControlCode1
Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent SystemCode1
SRMT: Shared Memory for Multi-agent Lifelong PathfindingCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMACCode1
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
HyperMARL: Adaptive Hypernetworks for Multi-Agent RLCode1
Learning to Cooperate with Humans using Generative AgentsCode1
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmaCode1
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement LearningCode1
PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal ControlCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified