SOTAVerified

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

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Showing 101–150 of 1718 papers

TitleStatusHype
Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement LearningCode3
Predicting Multi-Agent Specialization via Task Parallelizability—0
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario GenerationCode1
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL—0
SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social DilemmasCode2
A Generalist Hanabi AgentCode0
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
LLM-Mediated Guidance of MARL Systems—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control—0
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy—0
H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic—0
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping—0
ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement LearningCode2
Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse—0
Using a single actor to output personalized policy for different intersections—0
Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning—0
Fully-Decentralized MADDPG with Networked Agents—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—0
Pretrained LLMs as Real-Time Controllers for Robot Operated Serial Production Line—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention MechanismsCode0
Trajectory-Class-Aware Multi-Agent Reinforcement LearningCode1
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality—0
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization—0
Real-World Deployment and Assessment of a Multi-Agent Reinforcement Learning-Based Variable Speed Limit Control SystemCode0
Factorized Deep Q-Network for Cooperative Multi-Agent Reinforcement Learning in Victim Tagging—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning—0
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement LearningCode1
A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigation—0
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehiclesCode1
Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment—0
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement LearningCode0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Facilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment GeneralizationCode0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Q-MARL: A GRAPH-BASED SOLUTION FOR LARGE-SCALE MULTI-AGENT REINFORCEMENT LEARNING INSPIRED BY QUANTUM CHEMISTRY—0
Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified