SOTAVerified

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 1426–1450 of 1718 papers

TitleStatusHype
On the Convergence of Consensus Algorithms with Markovian Noise and Gradient Bias—0
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
The reinforcement learning-based multi-agent cooperative approach for the adaptive speed regulation on a metallurgical pickling line—0
REMAX: Relational Representation for Multi-Agent Exploration—0
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells—0
The Emergence of Adversarial Communication in Multi-Agent Reinforcement LearningCode1
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
QPLEX: Duplex Dueling Multi-Agent Q-LearningCode1
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Value-Decomposition Multi-Agent Actor-CriticsCode1
Off-Policy Multi-Agent Decomposed Policy GradientsCode1
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Reinforcement Communication Learning in Different Social Network StructuresCode0
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System—0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks—0
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement LearningCode1
Reward Machines for Cooperative Multi-Agent Reinforcement LearningCode1
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games—0
Online Multi-agent Reinforcement Learning for Decentralized Inverter-based Volt-VAR Control—0
QTRAN++: Improved Value Transformation for 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