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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 341–350 of 1718 papers

TitleStatusHype
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Decentralized scheduling through an adaptive, trading-based multi-agent system—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—0
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control—0
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent 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