Category: Deep Reinforcement Learning Mathematical Modelling Research Publications Robotics
Date: October 1, 2020
Prakash Mallick, Zhiyong Chen, Mohsen Zamani
Guided policy search algorithms have been proven to work with incredible accuracy for not only controlling a complicated dynamical system, but also learning optimal policies from various unseen instances. One assumes true nature of the states in almost all of the well known policy search and learning algorithms. This paper deals with a trajectory optimization procedure for an unknown dynamical system subject to measurement noise using expectation maximization and extends it to learning (optimal) policies which have less noise because of lower variance in the optimal trajectories. Theoretical and empirical evidence of learnt optimal policies of the new approach is depicted in comparison to some well known baselines which are evaluated on an autonomous system with widely used performance metrics.
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