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Model-Based Reinforcement Learning with Continuous States and Actions

Marc Peter Deisenroth; Carl Edward Rasmussen; Jan Peters
In: 16th European Symposium on Artificial Neural Networks, Proceedings. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-2008), April 23-25, Bruges, Belgium, Pages 19-24, ESANN, 2008.


Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable. GPDP is an approximate dynamic programming algorithm based on Gaussian process (GP) models for the value functions. In this paper, we extend GPDP to the case of unknown transition dynamics. After building a GP model for the transition dynamics, we apply GPDP to this model and determine a continuous-valued policy in the entire state space. We apply the resulting controller to the underpowered pendulum swing up. Moreover, we compare our results on this RL task to a nearly optimal discrete DP solution in a fully known environment.

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