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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 681–690 of 1718 papers

TitleStatusHype
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay—0
Independent Learning in Constrained Markov Potential GamesCode0
Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network—0
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making—0
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning—0
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling—0
SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks—0
Modelling crypto markets by multi-agent reinforcement learningCode0
Conservative and Risk-Aware Offline Multi-Agent Reinforcement LearningCode0
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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