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

Showing 16011610 of 1718 papers

TitleStatusHype
Universal Policies to Learn Them AllCode0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Carbon Market Simulation with Adaptive Mechanism DesignCode0
Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?Code0
Cooperative Artificial IntelligenceCode0
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile RobotsCode0
Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel DecodingCode0
Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement LearningCode0
Learning to Share and Hide Intentions using Information RegularizationCode0
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Benchmark Results

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