Q-learning for POMDP: An application to learning locomotion gaits

Tixian Wang; Amirhossein Taghvaei; Prashant G. Mehta

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arXiv · 1910.00107

Q-learning for POMDP: An application to learning locomotion gaits

Tixian Wang·Amirhossein Taghvaei·Prashant G. Mehta

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Abstract

This paper presents a Q-learning framework for learning optimal locomotion gaits in robotic systems modeled as coupled rigid bodies. Inspired by prevalence of periodic gaits in bio-locomotion, an open loop periodic input is assumed to (say) affect a nominal gait. The learning problem is to learn a new (modified) gait by using only partial noisy measurements of the state. The objective of learning is to maximize a given reward modeled as an objective function in optimal control settings. The proposed control architecture has three main components: (i) Phase modeling of dynamics by a single phase variable; (ii) A coupled oscillator feedback particle filter to represent the posterior distribution of the phase conditioned in the sensory measurements; and (iii) A Q-learning algorithm to learn the approximate optimal control law. The architecture is illustrated with the aid of a planar two-body system. The performance of the learning is demonstrated in a simulation environment.

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BibTeX RIS

Tixian Wang, Amirhossein Taghvaei, Prashant G. Mehta. 2019-09-30. Q-learning for POMDP: An application to learning locomotion gaits. https://arxiv.org/abs/1910.00107

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