  Projective simulation (PS) is a model for intelligent agents with a
deliberation capacity that is based on episodic memory. The model has been
shown to provide a flexible framework for constructing reinforcement-learning
agents, and it allows for quantum mechanical generalization, which leads to a
speed-up in deliberation time. PS agents have been applied successfully in the
context of complex skill learning in robotics, and in the design of
state-of-the-art quantum experiments. In this paper, we study the performance
of projective simulation in two benchmarking problems in navigation, namely the
grid world and the mountain car problem. The performance of PS is compared to
standard tabular reinforcement learning approaches, Q-learning and SARSA. Our
comparison demonstrates that the performance of PS and standard learning
approaches are qualitatively and quantitatively similar, while it is much
easier to choose optimal model parameters in case of projective simulation,
with a reduced computational effort of one to two orders of magnitude. Our
results show that the projective simulation model stands out for its simplicity
in terms of the number of model parameters, which makes it simple to set up the
learning agent in unknown task environments.
