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 1226–1250 of 1718 papers

TitleStatusHype
A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives—0
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning—0
Active flow control for three-dimensional cylinders through deep reinforcement learning—0
ADAGE: A generic two-layer framework for adaptive agent based modelling—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things—0
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach—0
Adaptive Learning Rates for Multi-Agent Reinforcement Learning—0
Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation—0
Adaptive parameter sharing for multi-agent reinforcement learning—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—0
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework—0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning—0
Adversarial attacks in consensus-based multi-agent reinforcement learning—0
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection—0
A finite time analysis of distributed Q-learning—0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior—0
A General Framework for Learning Mean-Field Games—0
A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigation—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