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In quadruped gait learning, policy search methods that scale high dimensionalcontinuous action spaces are commonly used. In most approaches, it is necessaryto introduce prior knowledge on the gaits to limit the highly non-convex searchspace of the policies. In this work, we propose a new approach to encode thesymmetry properties of the desired gaits, on the initial covariance of theGaussian search distribution, allowing for strategic exploration. Usingepisode-based likelihood ratio policy gradient and relative entropy policysearch, we learned the gaits walk and trot on a simulated quadruped. Comparingthese gaits to random gaits learned by initialized diagonal covariance matrix,we show that the performance can be significantly enhanced.

Learning walk and trot from the same objective using different types of exploration