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Uncertainty Estimation in Continuous Models applied to Reinforcement Learning


We consider the model-based reinforcement learning framework where we are interested in learning a model and control policy for a given objective. We consider modeling the dynamics of an environment using Gaussian Processes or a Bayesian neural network. For Bayesian neural networks we must define how to estimate uncertainty through a neural network and propagate distributions in time. Once we have a continuous model we can apply standard optimal control techniques to learn a policy. We consider the policy to be a radial basis policy and compare it's performance given the different models on a pendulum environment.

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