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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 1301–1310 of 1718 papers

TitleStatusHype
Sequential Communication in Multi-Agent Reinforcement Learning—0
Surprise Minimizing Multi-Agent Learning with Energy-based Models—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates—0
Dimension-Free Rates for Natural Policy Gradient in 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