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.