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 451–475 of 1718 papers

TitleStatusHype
An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management—0
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination DynamicsCode0
Experience-replay Innovative Dynamics—0
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and AssistanceCode0
ADAGE: A generic two-layer framework for adaptive agent based modelling—0
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL—0
Networked Agents in the Dark: Team Value Learning under Partial Observability—0
A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management—0
Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through Multi-Agent Reinforcement LearningCode0
Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning—0
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms—0
Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning—0
Turn-based Multi-Agent Reinforcement Learning Model Checking—0
CORD: Generalizable Cooperation via Role Diversity—0
PIMAEX: Multi-Agent Exploration through Peer Incentivization—0
Symmetries-enhanced Multi-Agent Reinforcement Learning—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Generative Emergent Communication: Large Language Model is a Collective World Model—0
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics—0
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning—0
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding—0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning—0
The impact of behavioral diversity in multi-agent reinforcement learning—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