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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 401–425 of 1718 papers

TitleStatusHype
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 with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Deep Reinforcement Learning, a textbook—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges—0
Decentralized Q-Learning in Zero-sum Markov Games—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport—0
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning—0
Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observability—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
Density-Aware Reinforcement Learning to Optimise Energy Efficiency in UAV-Assisted Networks—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
Dependent Multi-Task Learning with Causal Intervention for Image Captioning—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Double Distillation Network for Multi-Agent Reinforcement Learning—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
Dialogue Management based on Multi-domain Corpus—0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
Difference Rewards Policy Gradients—0
Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning—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