LEARNING TO SCHEDULE COMMUNICATION IN MULTI-AGENT REINFORCEMENT LEARNING

The Canadian Dataverse Repository2026-03-28 收录

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https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/XYNXBZ

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资源简介：

Daewoo Kim, 2025, "LEARNING TO SCHEDULE COMMUNICATION IN MULTI-AGENT REINFORCEMENT LEARNING", https://doi.org/10.5683/SP3/XYNXBZ, Borealis, V1

创建时间：

2025-10-15

LEARNING TO SCHEDULE COMMUNICATION IN MULTI-AGENT REINFORCEMENT LEARNING

Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents’ interaction, where well-coordinated actions among the agents are crucial to achieve

DataCite Commons2025-11-20 更新40

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and l

DataCite Commons2025-11-20 更新100

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park, 2025, "ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning", https://doi.org/10.5683/SP3/9Z9NAU, Borealis, V1

The Canadian Dataverse Repository00

Multi-Agent Reinforcement Learning

The dataset used in the paper is a collection of 21 diverse multi-agent tasks in four environments: level-based foraging, boulder-push, multi-robot warehouse, and multi-agent particle environment.

DataCite Commons2024-12-16 更新50

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

The Canadian Dataverse Repository2025-01-01 更新00