Probabilistic inference for determining options in reinforcement learning.

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Bibliographic Details
Title:	Probabilistic inference for determining options in reinforcement learning.
Authors:	Daniel, Christian Christian.Daniel@de.bosch.com, van Hoof, Herke¹, Peters, Jan, Neumann, Gerhard¹
Source:	Machine Learning. Sep2016, Vol. 104 Issue 2-3, p337-357. 21p.
Subjects:	Probabilistic inference, Reinforcement learning, Machine learning, Partially observable Markov decision processes, Algorithms
Abstract:	Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to alleviate these concerns. Instead of learning a single monolithic policy, the agent learns a set of simpler sub-policies as well as the initiation and termination probabilities for each of those sub-policies. While existing option learning algorithms frequently require manual specification of components such as the sub-policies, we present an algorithm which infers all relevant components of the option framework from data. Furthermore, the proposed approach is based on parametric option representations and works well in combination with current policy search methods, which are particularly well suited for continuous real-world tasks. We present results on SMDPs with discrete as well as continuous state-action spaces. The results show that the presented algorithm can combine simple sub-policies to solve complex tasks and can improve learning performance on simpler tasks. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Tasks that require many sequential decisions or complex solutions are hard to solve using conventional reinforcement learning algorithms. Based on the semi Markov decision process setting (SMDP) and the option framework, we propose a model which aims to alleviate these concerns. Instead of learning a single monolithic policy, the agent learns a set of simpler sub-policies as well as the initiation and termination probabilities for each of those sub-policies. While existing option learning algorithms frequently require manual specification of components such as the sub-policies, we present an algorithm which infers all relevant components of the option framework from data. Furthermore, the proposed approach is based on parametric option representations and works well in combination with current policy search methods, which are particularly well suited for continuous real-world tasks. We present results on SMDPs with discrete as well as continuous state-action spaces. The results show that the presented algorithm can combine simple sub-policies to solve complex tasks and can improve learning performance on simpler tasks. [ABSTRACT FROM AUTHOR]
ISSN:	08856125
DOI:	10.1007/s10994-016-5580-x