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 691–700 of 1718 papers

TitleStatusHype
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Understanding Model Selection For Learning In Strategic Environments—0
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation—0
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov GamesCode0
Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback—0
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning SystemsCode0
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems—0
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs—0
O(T^-1) Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games—0
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

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