RI: Collaborative Research: Feature Discovery and Benchmarks for Exportable Reinforcement Learning

协作提案对：0713435(主要)&amp；0713148“协作：RI：可输出强化学习的特征发现和基准”PI：罗纳德·帕尔，杜克大学PI：迈克尔·L·利特曼，罗格斯大学摘要本项目关注强化学习背景下自动特征发现的几个方面。选择不当的特征会导致强化学习算法失败，因此，只有擅长构建特征的个人才能为新任务创建成功的强化学习系统。这个问题突出了现有研究中的两个缺陷。首先，大多数现有的强化学习方法不能自动和稳健地生成或发现特征。第二，现有的基准问题和基准测试的范例没有充分区分聪明的算法设计和聪明的特征工程。本项目从双管齐下解决这些挑战。第一个目标是推进一项技术议程，从而产生一种新的特征发现和模型表示方法。第二个方面是开发与现有工作不同的重点和结构的基准方法和存储库。基准工作的目标将是产生一套公平和可重复的实验，这些实验将有助于阐明现有方法的优缺点，同时引入挑战，以推动新方法的开发。

Collaborative Proposal pair: 0713435 (Lead) & 0713148"Collaborative: RI: Feature Discovery and Benchmarks for exportable Reinforcement Learning"PI: Ronald Parr, Duke UniversityPI: Michael L. Littman, Rutgers UniversityABSTRACTThis project focuses on several aspects of automated feature discovery in the context of reinforcement learning. Badly chosen features cause reinforcement-learning algorithms to fail and, as such, only individuals skilled in feature construction can create successful reinforcement-learning systems for novel tasks. This issue underscores two shortcomings in existing research. First, most existing reinforcement-learning methods cannot generate or discover features automatically and robustly. Second, existing benchmark problems and paradigms for benchmarking do not distinguish adequately between clever algorithm design and clever feature engineering.This project addresses these challenges in two-pronged approach. The first prong aims to advance a technical agenda leading to a new approach to feature discovery and model representation. The second prong is the development of a benchmark methodology and repository with a different focus and structure from existing endeavors. The goal for the benchmarking effort will be to produce a set of fair and reproducible experiments that will help elucidate the strengths and weaknesses of existing approaches, while simultaneously introducing challenges to motivate the development of new approaches.