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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

Showing 76100 of 1718 papers

TitleStatusHype
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement LearningCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
ALMA: Hierarchical Learning for Composite Multi-Agent TasksCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Cooperation and Fairness in Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-NCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Collaborative Visual NavigationCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified