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Title: Value-based subgoal discovery and path planning for reaching long-horizon goals
Authors: Pateria, Shubham
Subagdja, Budhitama
Tan, Ah-Hwee
Quek, Chai
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Pateria, 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.
Journal: IEEE Transactions on Neural Networks and Learning Systems
Abstract: Learning 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.
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2023.3240004
Schools: School of Computer Science and Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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