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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 411–420 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems—0
Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs—0
Distributional Black-Box Model Inversion Attack with Multi-Agent Reinforcement Learning—0
MAexp: A Generic Platform for RL-based Multi-Agent ExplorationCode2
Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution—0
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks—0
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal HypergraphsCode0
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—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