In this contribution we present an extension of a neuroevolutionary method called Evolutionary Acquisition of Neural Topologies (EANT) [11] that allows the evolution of solutions taking the form of a POMDP agent (Partially Observable Markov Decision Process) [8]. The solution we propose involves cascading a Kalman filter [10] (state estimator) and a feed-forward neural network. The extension (EANT+KALMAN) has been tested on the double pole balancing without velocity benchmark, achieving significantly better results than the to date published results of other algorithms.
['Yohannes Kassahun', 'Jose de Gea', 'Jan Hendrik Metzen', 'Mark Edgington', 'Frank Kirchner']
EANT+KALMAN: An Efficient Reinforcement Learning Method for Continuous State Partially Observable Domains