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 701–725 of 1718 papers

TitleStatusHype
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Decentralized Learning Strategies for Estimation Error Minimization with Graph Neural Networks—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure—0
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning—0
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Dealing with Non-Stationarity in MARL via Trust-Region Decomposition—0
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Group-Agent Reinforcement Learning—0
Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space—0
Improved cooperation by balancing exploration and exploitation in intertemporal social dilemma tasks—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
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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