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

2024-03-01 14:18:46
Nathan Gavenski, Michael Luck, Odinaldo Rodrigues

Abstract

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: this https URL

Abstract (translated)

翻译:模仿学习领域需要专家数据来训练代理在任务中。通常,这种学习方法因缺乏可用数据而受到限制,导致在数据集上测试各种技术。创建数据集是一个费力且耗时的过程,需要研究人员从头开始训练专家代理,记录他们的交互,并使用新创建的数据测试每个基准方法。此外,为每个新技术创建新的数据集会导致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以极大地不同。为了应对这些问题,这项工作旨在通过创建模仿学习数据集来解决这些问题,一个工具包,允许:(i)经过多线程支持的精心挑选的专家策略;(ii)准确测量并且 readily available 的数据集和技术;(iii)分享常见的模仿学习技术的实现。演示链接:this <https://url>

URL

https://arxiv.org/abs/2403.00550

PDF

https://arxiv.org/pdf/2403.00550.pdf


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