Multi-Task Option Learning and Discovery for Stochastic Path Planning

• URL: http://arxiv.org/abs/2210.00068v1
• Date: Fri, 30 Sep 2022 19:57:52 GMT
• Title: Multi-Task Option Learning and Discovery for Stochastic Path Planning
• Authors: Naman Shah, Siddharth Srivastava
• Abstract summary: This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon path planning problems. Our approach computes useful options with policies as well as high-level paths that compose the discovered options. We show that this approach yields strong guarantees of executability and solvability.
• Score: 27.384742641275228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a stochastic dynamics simulator and an occupancy matrix of the environment, our approach computes useful options with policies as well as high-level paths that compose the discovered options. Our main contributions are (1) data-driven methods for creating abstract states that serve as endpoints for helpful options, (2) methods for computing option policies using auto-generated option guides in the form of dense pseudo-reward functions, and (3) an overarching algorithm for composing the computed options. We show that this approach yields strong guarantees of executability and solvability: under fairly general conditions, the computed option guides lead to composable option policies and consequently ensure downward refinability. Empirical evaluation on a range of robots, environments, and tasks shows that this approach effectively transfers knowledge across related tasks and that it outperforms existing approaches by a significant margin.

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