A multi-agent reinforcement learning scheduling algorithm integrating state graph and task graph structural modeling for ride-sharing dispatching

Nature·Jan 17

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring the use of reinforcement learning algorithms for various scheduling applications. The algorithms being developed integrate different techniques, such as deep learning and particle swarm optimization, to optimize tasks in areas like ride-sharing, traffic signals, and cloud computing. The specific focus and outcomes of these efforts are not clearly defined, with multiple approaches and applications being investigated.

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Optimized multi agent reinforcement learning algorithms with hybrid BiLSTM for cost efficient EV charging scheduling

Frontiers·Jan 4

Dynamic traffic signal scheduling system based on adaptive quad agent Double Deep Q -network algorithm

Frontiers·Jun 16

Deep neural network-based coupling model of inter-organizational knowledge flow and agent collaborative decision-making

Nature·Feb 2

A fusion deep Q-learning and particle swarm optimization algorithm for adaptive resource allocation in cloud computing circumstances

Nature·Apr 16

Multi modal hierarchical reinforcement learning framework for dynamic sports sponsorship optimization

Nature·Dec 18

Energy optimized scheduling in wireless sensor networks (WSNs) using hybrid bio-inspired reinforcement learning approach

Nature·Mar 11

Deep reinforcement learning for scheduling semiconductor cluster tools in varying configurations

Nature·Dec 17

Reinforcement learning based multi objective task scheduling for energy efficient and cost effective cloud edge computing

Nature·Nov 24

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