Experimental study of the eligibility traces in complex valued reinforcement learning

Effectiveness of eligibility traces in complex valued reinforcement learning is studied. Complex valued reinforcement learning is a new method inspired by complex valued neural networks. In this study, it is desired that various approaches in the ordinally real valued reinforcement learning are applied to the complex valued reinforcement learning. This paper focuses attention on an experimental study of the eligibility traces. Simulation results infer that there is a possibility of overcoming tight perceptual aliasing with long trace back up.