Analysis of reinforcement learning strategies for predation in a mimic-model prey environment

In this paper we propose a mathematical learning model for a stochastic automaton simulating the behaviour of a predator operating in a random environment occupied by two types of prey: palatable mimics and unpalatable models. Specifically, a well known linear reinforcement learning algorithm is used to update the probabilities of the two actions, eat prey or ignore prey, at every random encounter. Each action elicits a probabilistic response from the environment that can be either favorable or unfavourable. We analyse both fixed and varying stochastic responses for the system. The basic approach of mimicry is defined and a short review of relevant previous approaches in the literature is given. Finally, the conditions for continuous predator performance improvement are explicitly formulated and precise definitions of predatory efficiency and mimicry efficiency are also provided.