Learning Options with Interest Functions

Authors

  • Khimya Khetarpal McGill University
  • Doina Precup McGill University

DOI:

https://doi.org/10.1609/aaai.v33i01.33019955

Abstract

Learning temporal abstractions which are partial solutions to a task and could be reused for solving other tasks is an ingredient that can help agents to plan and learn efficiently. In this work, we tackle this problem in the options framework. We aim to autonomously learn options which are specialized in different state space regions by proposing a notion of interest functions, which generalizes initiation sets from the options framework for function approximation. We build on the option-critic framework to derive policy gradient theorems for interest functions, leading to a new interest-option-critic architecture.

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Published

2019-07-17

How to Cite

Khetarpal, K., & Precup, D. (2019). Learning Options with Interest Functions. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9955-9956. https://doi.org/10.1609/aaai.v33i01.33019955

Issue

Section

Student Abstract Track