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 326–350 of 1718 papers

TitleStatusHype
Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convectionCode0
Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization—0
Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous RoboticsCode0
Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and qualityCode0
Reinforced Prompt Personalization for Recommendation with Large Language ModelsCode1
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control—0
POGEMA: A Benchmark Platform for Cooperative Multi-Agent PathfindingCode1
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models—0
Navigating the Smog: A Cooperative Multi-Agent RL for Accurate Air Pollution Mapping through Data Assimilation—0
Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource AllocationCode2
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Ontology-driven Reinforcement Learning for Personalized Student Support—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance—0
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language ModelsCode1
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
A Review of the Applications of Deep Learning-Based Emergent Communication—0
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System—0
Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)—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