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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

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Showing 601–650 of 1718 papers

TitleStatusHype
The Benefits of Power Regularization in Cooperative Reinforcement Learning—0
Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs—0
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning—0
Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors—0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Review—0
Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation—0
Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy—0
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles—0
Safe Multi-agent Reinforcement Learning with Natural Language Constraints—0
Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement LearningCode0
Mutation-Bias Learning in Games—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
Variational Offline Multi-agent Skill Discovery—0
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum ChannelsCode0
A finite time analysis of distributed Q-learning—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—0
Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning—0
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement LearningCode0
Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks—0
Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation StudyCode0
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning—0
Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization—0
SocialGFs: Learning Social Gradient Fields for Multi-Agent Reinforcement Learning—0
MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games—0
MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure—0
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning—0
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty—0
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems—0
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning—0
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal HypergraphsCode0
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
Differentially Private Reinforcement Learning with Self-Play—0
Attention-Driven Multi-Agent Reinforcement Learning: Enhancing Decisions with Expertise-Informed Tasks—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