arXiv 2403.005502024-03-01cs.LGcs.AI

Imitation Learning Datasets：用于创建数据集、训练智能体和基准测试的工具包

Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking

University of Sussex（萨塞克斯大学）

Nathan Gavenski, Michael Luck, Odinaldo Rodrigues

备注：: his paper has been accepted in the demonstration track for the 23rd International Conference on Autonomous Agents and Multi-Agent Systems

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AI总结：

本文提出 Imitation Learning Datasets 工具包，通过多线程专家策略、现成数据集与技术及共享实现，解决模仿学习数据创建繁琐和评估不一致的问题。

摘要：

模仿学习领域需要专家数据来在任务中训练智能体。通常情况下，这种学习方法受限于缺乏可用数据，导致各项技术只能在其自有数据集上接受测试。创建数据集是一个繁琐的过程，研究人员需要从头训练专家智能体、记录它们的交互，并用新创建的数据测试每一种基准方法。此外，为每种新技术创建新数据集会造成评估过程缺乏一致性，因为每个数据集在状态和动作分布上可能存在巨大差异。针对这些问题，本工作旨在通过创建 Imitation Learning Datasets 来解决上述问题，这是一个支持以下功能的工具包：（i）经过整理的专家策略，并提供多线程支持以加快数据集创建；（ii）随时可用的数据集和技术，并带有精确测量结果；以及（iii）共享常见模仿学习技术的实现。演示链接：https://nathangavenski.github.io/#/il-datasets-video

英文摘要：

Imitation learning field requires expert data to train agents in a task. Most often, this learning approach suffers from the absence of available data, which results in techniques being tested on its dataset. Creating datasets is a cumbersome process requiring researchers to train expert agents from scratch, record their interactions and test each benchmark method with newly created data. Moreover, creating new datasets for each new technique results in a lack of consistency in the evaluation process since each dataset can drastically vary in state and action distribution. In response, this work aims to address these issues by creating Imitation Learning Datasets, a toolkit that allows for: (i) curated expert policies with multithreaded support for faster dataset creation; (ii) readily available datasets and techniques with precise measurements; and (iii) sharing implementations of common imitation learning techniques. Demonstration link: https://nathangavenski.github.io/#/il-datasets-video

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摘要：

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