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arXiv · 1712.00004

Learnings Options End-to-End for Continuous Action Tasks

Martin Klissarov·Pierre-Luc Bacon·Jean Harb·Doina Precup
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Abstract

We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]). In orderto achieve this goal we work with the option-critic architecture (Baconet al.[2017])using a deliberation cost and train it with proximal policy optimization (Schulmanet al.[2017]) instead of vanilla policy gradient. Results on Mujoco domains arepromising, but lead to interesting questions aboutwhena given option should beused, an issue directly connected to the use of initiation sets.

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BibTeXRIS

Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup. 2017-11-30. Learnings Options End-to-End for Continuous Action Tasks. https://arxiv.org/abs/1712.00004

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