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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 776–800 of 1718 papers

TitleStatusHype
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization—0
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control—0
Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning—0
Kindness in Multi-Agent Reinforcement Learning—0
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles—0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning—0
LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
Learning Homophilic Incentives in Sequential Social Dilemmas—0
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach—0
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming—0
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control—0
Last Iterate Convergence in Monotone Mean Field Games—0
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
Federated Dynamic Spectrum Access—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