This article introduces a new form of ethogram that provides a basis for studying reinforcement learning in biologically inspired collective robotics systems. In general, an ethogram is a record of behavior patterns, which has grown out of ethology (ways to explain agent behavior). The rough set approach introduced by Zdzislaw Pawlak in 1982 provides a ground for deriving pattern-based rewards in the context of an approximation space. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards used to compute action rewards as well as action preferences. A brief description of a prototype of an ecosystem testbed used to record ethograms in a dynamically changing system of agents is presented. The contribution of this article is an introduction to an ethological approach to the study of action preferences and action rewards during reinforcement learning in intelligent systems considered in the context of approximation spaces.
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