Source
Brain Informatics
DATE OF PUBLICATION
09/15/2021
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Intrinsic Motivation to Learn Action-State Representation with Hierarchical Temporal Memory
Model-based reinforcement learning,
Intrinsic motivation,
Hierarchical temporal memory,
Sparse distributed representations
Abstract
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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