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 661–670 of 1718 papers

TitleStatusHype
Cooperative Actor-Critic via TD Error Aggregation—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management—0
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic—0
Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization—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