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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 641–650 of 1718 papers

TitleStatusHype
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles—0
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics—0
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning—0
Generalisable Agents for Neural Network Optimisation—0
Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning—0
Unsynchronized Decentralized Q-Learning: Two Timescale Analysis By Persistence—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
General sum stochastic games with networked information flows—0
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified