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 426–450 of 1718 papers

TitleStatusHype
Who is Helping Whom? Analyzing Inter-dependencies to Evaluate Cooperation in Human-AI Teaming—0
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning—0
TAR^2: Temporal-Agent Reward Redistribution for Optimal Policy Preservation in Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Focal Diversity OptimizationCode0
Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents—0
Deep Meta Coordination Graphs for Multi-agent Reinforcement LearningCode0
Reinforcement Learning on Dyads to Enhance Medication Adherence—0
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning—0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
Learning Efficient Flocking Control based on Gibbs Random Fields—0
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement LearningCode0
Energy-Efficient Flying LoRa Gateways: A Multi-Agent Reinforcement Learning Approach—0
Optimistic ε-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
Sequential Multi-objective Multi-agent Reinforcement Learning Approach for Predictive Maintenance—0
Visual Theory of Mind Enables the Invention of Proto-Writing—0
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement LearningCode0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Learning Mean Field Control on Sparse Graphs—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—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