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arXiv · 1911.07224

Learning from Trajectories via Subgoal Discovery

Sujoy Paul·Jeroen van Baar·Amit K. Roy-Chowdhury
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Abstract

Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples. In such cases, using a set of expert trajectories could help to learn faster. However, Imitation Learning (IL) via supervised pre-training with these trajectories may not perform as well and generally requires additional finetuning with expert-in-the-loop. In this paper, we propose an approach which uses the expert trajectories and learns to decompose the complex main task into smaller sub-goals. We learn a function which partitions the state-space into sub-goals, which can then be used to design an extrinsic reward function. We follow a strategy where the agent first learns from the trajectories using IL and then switches to Reinforcement Learning (RL) using the identified sub-goals, to alleviate the errors in the IL step. To deal with states which are under-represented by the trajectory set, we also learn a function to modulate the sub-goal predictions. We show that our method is able to solve complex goal-oriented tasks, which other RL, IL or their combinations in literature are not able to solve.

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Sujoy Paul, Jeroen van Baar, Amit K. Roy-Chowdhury. 2019-11-03. Learning from Trajectories via Subgoal Discovery. https://arxiv.org/abs/1911.07224

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