Selective Perception As a Mechanism To Adapt Agents To The Environment: An Evolutionary Approach

Rapid advancement of machine learning makes it possible to consider large amounts of data to learn from.Learning agents may get data ranging on real intervals directly from the environment they interact with, in a process usually time-expensive. To improve learning and manage these data,approximated models and memory mechanisms are adopted. Inmost of the implementations of reinforcement learning facing this type of data, approximation is obtained by neural networks and the process of drawing information from data is mediated by a short-term memory that stores the previous experiences for additional re-learning, to speed-up the learning process,mimicking what is done by people.In this work, we are proposing a novel computational approachable to selectively filter the information, or cognitive load, for the agent’s short-term memory, thus emulating the attention mechanism characteristic of human perception. We devised an evolutionary model of agent’s perception to adapt the attention filter present in the proposed architecture to the actual environment faced by the agent, by selecting the experiences that most likely influence in a positive way its learning characteristics. This approach can evolve a filter able to provide an optimal cognitive load of the experiences entering in the agent’s short-term memory of a limited capacity. The evolved sampling dynamics can also lead to the emergence of intrinsically motivated curiosity.