Brain Informatics
Год публикации
Александр Панов Петр Кудеров Евгений Дживеликян Артем Латышев

Intrinsic Motivation to Learn Action-State Representation with Hierarchical Temporal Memory


In this paper, we propose a biologically plausible model for learning the decision-making sequence in an external environment with internal motivation. As a computational model, we propose a hierarchical architecture of an intelligent agent acquiring experience based on reinforcement learning. We use the basal ganglia model to aggregate a reward, and sparse distributed representation of states and actions in hierarchical temporal memory elements. The proposed architecture allows the agent to build a compact model of the environment and to form an effective strategy, which is experimentally demonstrated to search for resources in grid environments.

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