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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 361370 of 1718 papers

TitleStatusHype
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement LearningCode0
Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs0
The Benefits of Power Regularization in Cooperative Reinforcement Learning0
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement LearningCode1
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and EvaluationCode3
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning0
Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Review0
Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation0
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Benchmark Results

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