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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 1411–1420 of 1718 papers

TitleStatusHype
Agent Environment Cycle Games—0
Ultra-dense Low Data Rate (UDLD) Communication in the THz—0
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control—0
Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response—0
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Multi-Agent Reinforcement Learning in Cournot Games—0
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—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