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 671–680 of 1718 papers

TitleStatusHype
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
Coordination Failure in Cooperative Offline MARL—0
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Correcting Experience Replay for Multi-Agent Communication—0
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL—0
FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning—0
AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning—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