Contributed talk: Safe Policy Search with Gaussian Process Models
Kyriakos Polymenakos · Stephen J Roberts
2017 Talk
in
Workshop: Transparent and interpretable Machine Learning in Safety Critical Environments
in
Workshop: Transparent and interpretable Machine Learning in Safety Critical Environments
Abstract
We propose a method to optimise the parameters of a policy which will be used to safely perform a given task in a data-efficient manner. We train a Gaussian process model to capture the system dynamics, based on the PILCO framework. Our model has useful analytic properties, which allow closed form computation of error gradients and estimating the probability of violating given state space constraints. During training, as well as operation, only policies that are deemed safe are implemented on the real system, minimising the risk of failure.
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