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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 1181–1190 of 1718 papers

TitleStatusHype
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Sequential Communication in Multi-Agent Reinforcement Learning—0
Greedy-based Value Representation for Efficient Coordination in Multi-agent Reinforcement Learning—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning—0
LPMARL: Linear Programming based Implicit Task Assigment for Hiearchical Multi-Agent Reinforcement Learning—0
Surprise Minimizing Multi-Agent Learning with Energy-based Models—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—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