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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 351–375 of 1718 papers

TitleStatusHype
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation—0
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation—0
An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks—0
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL—0
An Algorithm For Adversary Aware Decentralized Networked MARL—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning—0
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Biases for Emergent Communication in Multi-agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—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