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 476–500 of 1718 papers

TitleStatusHype
The impact of behavioral diversity in multi-agent reinforcement learning—0
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning—0
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs—0
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning—0
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic—0
Steganography in Game Actions—0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization—0
Augmenting the action space with conventions to improve multi-agent cooperation in HanabiCode0
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning—0
Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles—0
Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach—0
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis—0
Comparative Analysis of Multi-Agent Reinforcement Learning Policies for Crop Planning Decision Support—0
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning—0
Towards Fault Tolerance in Multi-Agent Reinforcement LearningCode0
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control—0
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium—0
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem—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