Optimal Control with Reinforcement Learning using Reservoir Computing and Gaussian Mixture

Optimal control problems could be solved with reinforcement learning. However it is challenging to use it with continuous state and action spaces, not to speak about partially observable environments. In this paper we propose a reinforcement learning system for partially observable environments with continuous state and action spaces. The method utilizes novel machine learning methods, the Echo State Network, and the Incremental Gaussian Mixture Network.