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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 1121–1130 of 1718 papers

TitleStatusHype
Policy Optimization for Markov Games: Unified Framework and Faster Convergence—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning—0
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential GameCode0
Sample-Efficient Reinforcement Learning of Partially Observable Markov Games—0
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning—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