A learning strategy for predator preying on edible and inedible prey

In this paper I propose a reinforcement learning model for a predator preying upon two types of prey, the unpalatable (noxious) models, and the palatable mimics. The latter type of prey resembles the models in appearance so as to derive some protection from the predator who must avoid the unpalatable models. Essentially the predator is treated as a learning automaton adopting a simple reinforcement learning strategy in order to increase its consumption of palatable prey and reduce the consumption of unpalatable ones. The populations of both mimics and models are assumed to grow logistically.