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Optimal Control and Reinforcement Learning with Gaussian Process Models

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If you have a question about this talk, please contact Zoubin Ghahramani.

Optimal control and reinforcement learning (RL) have the same objective: optimization of a long-term performance measure. While the system in optimal control problems is usually known, RL has a more general setup, which includes possibly unknown environments. However, after learning a model standard algorithms for optimal control can also be applied to RL.

In this talk a generalization of dynamic programming (DP) to continuous-valued state and action spaces is given. The proposed algorithm (GPDP) combines Gaussian process (GP) models with DP and yields an approximate optimal closed-loop policy on the entire state space. We apply GPDP to the underactuated pendulum swing up. For exactly known environments we show that GPDP yields an close-to optimal solution. Moreover, we show that GPDP can successfully be applied to stochastic optimal control problems.

This talk is part of the Machine Learning @ CUED series.

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