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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 441–450 of 1718 papers

TitleStatusHype
Learning and communication pressures in neural networks: Lessons from emergent communication—0
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative DrivingCode0
Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies—0
Strategizing against Q-learners: A Control-theoretical Approach—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Ensembling Prioritized Hybrid Policies for Multi-agent PathfindingCode2
Generalising Multi-Agent Cooperation through Task-Agnostic CommunicationCode0
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence—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