{"type":"ScholarlyArticle","title":"Inter-Task Similarity for Lifelong Reinforcement Learning in Heterogeneous Tasks","authors":[{"name":"Sergio A. Serrano","slug":"sergio-a-serrano","given":"Sergio A.","family":"Serrano","affiliation":""}],"abstract":"Reinforcement learning (RL) is a learning paradigm in which an agent interacts with the environment it inhabits to learn in a trial-and-error way. By letting the agent acquire knowledge from its own experience, RL has been successfully applied to complex domains such as robotics. However, for non-trivial problems, training an RL agent can take very long periods of time. Lifelong machine learning (LML) is a learning setting in which the agent learns to solve tasks sequentially, by leveraging knowledge accumulated from previously solved tasks to learn better/faster in a new one. Most LML works heavily rely on the assumption that tasks are similar to each other. However, this may not be true for some domains with a high degree of task-diversity that could benefit from adopting a lifelong learning approach, e.g., service robotics. Therefore, in this research we will address the problem of learning to solve a sequence of RL heterogeneous tasks (i.e., tasks that differ in their state-action space).","doi":"10.24963/ijcai.2021/689","url":"https://ijcai.org/proceedings/2021/689","pdf":"https://ijcai.org/proceedings/2021/0689.pdf","markdown":"https://ijcai.org/proceedings/2021/689.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/689","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":689,"pages":{"first":4915,"last":4916},"keywords":["Machine Learning: Reinforcement Learning","Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Incremental Learning","Robotics: Learning in Robotics"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}