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dc.contributor.authorPateria, Shubhamen_US
dc.contributor.authorSubagdja, Budhitamaen_US
dc.contributor.authorTan, Ah-Hweeen_US
dc.contributor.authorQuek, Chaien_US
dc.identifier.citationPateria, S., Subagdja, B., Tan, A. & Quek, C. (2023). Value-based subgoal discovery and path planning for reaching long-horizon goals. IEEE Transactions On Neural Networks and Learning Systems.
dc.description.abstractLearning to reach long-horizon goals in spatial traversal tasks is a significant challenge for autonomous agents. Recent subgoal graph-based planning methods address this challenge by decomposing a goal into a sequence of shorter-horizon subgoals. These methods, however, use arbitrary heuristics for sampling or discovering subgoals, which may not conform to the cumulative reward distribution. Moreover, they are prone to learning erroneous connections (edges) between subgoals, especially those lying across obstacles. To address these issues, this article proposes a novel subgoal graph-based planning method called learning subgoal graph using value-based subgoal discovery and automatic pruning (LSGVP). The proposed method uses a subgoal discovery heuristic that is based on a cumulative reward (value) measure and yields sparse subgoals, including those lying on the higher cumulative reward paths. Moreover, LSGVP guides the agent to automatically prune the learned subgoal graph to remove the erroneous edges. The combination of these novel features helps the LSGVP agent to achieve higher cumulative positive rewards than other subgoal sampling or discovery heuristics, as well as higher goal-reaching success rates than other state-of-the-art subgoal graph-based planning methods.en_US
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.rights© 2023 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleValue-based subgoal discovery and path planning for reaching long-horizon goalsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.subject.keywordsMotion Planningen_US
dc.subject.keywordsPath Planningen_US
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