Supplementary file for Multi-tool Drilling Path Optimization by Multi-Agent Reinforcement Learning Approach

NIAID Data Ecosystem2026-05-01 收录

钻孔路径优化

多智能体强化学习

数据链接：

https://data.mendeley.com/datasets/cv8mf3nprx 数据链接链接失效反馈

官方服务：

资源简介：

The details of all workpieces can be checked in this file.

创建时间：

2023-06-26

Battlesnake Challenge Framework

多智能体强化学习

游戏开发

该数据集是基于Battlesnake框架开发的多智能体强化学习框架，它包含了用于训练和评估的模拟健身房环境。此外，该框架还提供了一系列训练示例，并支持在竞争环境中对智能体进行评估。该数据集涵盖了不同规模的智能体数量（4、5和6个）和可变地图大小（7x7、11x11、19x19）。其任务是进行具有人类参与循环的多智能体强化学习。

arXiv100

Replication Data for: Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning

多智能体强化学习

趋化行为模拟

Scripts used in the experiments and analysis presented in the paper.

DataCite Commons2025-08-06 更新70

Supplementary Dataset_A_Scalable_Privacy-Preserving_Multi-agent_Deep_Reinforcement_Learning_Approach_for_Large-Scale_Peer-to-Peer_Transactive_Energy_Trading

能源交易

多智能体强化学习

Data_For_A_Scalable_Privacy-Preserving_Multi-agent_Deep_Reinforcement_Learning_Approach_for_Large-Scale_Peer-to-Peer_Transactive_Energy _Trading

DataCite Commons2020-12-22 更新60

dataset for collaborative content caching algorithm

卫星网络内容缓存

多智能体强化学习

In this letter, we investigate the content caching problem within large-scale integrated satellite-terrestrial net works, focusing on a fusion scenario of future large-scale remote sensing constellati

IEEE80

Energy Management for Fuel Cell Electric Vehicle Based on Multi-agent Reinforcement Learning

电动汽车能量管理

多智能体强化学习

The coordinated optimization of adaptive cruise control (ACC) and energy management strategy (EMS) is promising for improving the performance of fuel cell electric vehicle (FCEV). This paper proposes

IEEE60

Supplementary file for Multi-tool Drilling Path Optimization by Multi-Agent Reinforcement Learning Approach

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