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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 651–675 of 1718 papers

TitleStatusHype
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning, a textbook—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
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Bi-level Mean Field: Dynamic Grouping for Large-Scale MARL—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—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
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Biases for Emergent Communication in Multi-agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum Learning—0
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning—0
DeepHive: A multi-agent reinforcement learning approach for automated discovery of swarm-based optimization policies—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning—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