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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 931–940 of 1718 papers

TitleStatusHype
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation—0
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning—0
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning—0
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus—0
Online Learning in Unknown Markov Games—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Provably Learning Nash Policies in Constrained Markov Potential Games—0
PTDE: Personalized Training with Distilled Execution for 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