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18-05-2020 | Original Article | Issue 10/2020

International Journal of Machine Learning and Cybernetics 10/2020

Evaluating skills in hierarchical reinforcement learning

Journal:
International Journal of Machine Learning and Cybernetics > Issue 10/2020
Authors:
Marzieh Davoodabadi Farahani, Nasser Mozayani
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Abstract

Despite the benefits mentioned in previous works of automatically acquiring skills for using them in hierarchical reinforcement learning algorithms such as solving the curse of dimensionality, improving exploration, and speeding up value propagation, they have not paid much attention to evaluating the effect of each skill on these factors. In this paper, we show that depending on the given task, a skill may be useful for learning it or not. In addition, the focus of the related work of automatically acquiring skills is on detecting subgoals, i.e., the skill termination condition, but there is not a precise method for extracting the initiation set of skills. In this paper, we propose not only two methods for evaluating skills but also two other methods for pruning the initiation set of them. Experimental results show significant improvements in learning different test domains after evaluating and pruning skills.

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