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 1476–1500 of 1718 papers

TitleStatusHype
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningCode0
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
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
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
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
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement LearningCode0
Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning—0
Cooperative Multi-Agent Reinforcement Learning with Partial Observations—0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Learning to Communicate Using Counterfactual Reasoning—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