Multi-Task Option Learning and Discovery for Stochastic Path Planning

Naman Shah, Siddharth Srivastava

Keywords: Option discovery, learning abstractions, planning and learning, reinforcement learning, RL for robotics, hierarchical methods, stochastic path planning

TL;DR: This paper presents a novel approach for learning reusable multi-options to compute solutions for stochastic path planning problems using an hierarchical approach that combines planning and learning.

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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Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)