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Guided policy search algorithms have been proven to work with incredibleaccuracy for not only controlling a complicated dynamical system, but alsolearning optimal policies from various unseen instances. One assumes truenature of the states in almost all of the well known policy search and learningalgorithms. This paper deals with a trajectory optimization procedure for anunknown dynamical system subject to measurement noise using expectationmaximization and extends it to learning (optimal) policies which have lessnoise because of lower variance in the optimal trajectories. Theoretical andempirical evidence of learnt optimal policies of the new approach is depictedin comparison to some well known baselines which are evaluated on an autonomoussystem with widely used performance metrics.

Reinforcement Learning Using Expectation Maximization Based Guided Policy Search for Stochastic Dynamics