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 1021–1030 of 1718 papers

TitleStatusHype
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning—0
RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning—0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Safe and Efficient CAV Lane Changing using Decentralised Safety Shields—0
Safe Bottom-Up Flexibility Provision from Distributed Energy Resources—0
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Safe Multi-agent Reinforcement Learning with Natural Language Constraints—0
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium—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