Planning with Hierarchical Temporal Memory for Deterministic Markov Decision Problem

Abstract

Sequential decision making is among the key problems in Artificial Intelligence. It can be formalized as Markov Decision Process (MDP). One approach to solve it, called model-based Reinforcement Learning (RL), combines learning the model of the environment and the global policy. Having a good model of the environment opens up such properties as data efficiency and targeted exploration. While most of the memorybased approaches are based on using Artificial Neural Networks (ANNs), in our work we instead draw the ideas from Hierarchical Temporal Memory (HTM) framework, which is based on human-like memory model. We utilize it to build an agent’s memory that learns the environment dynamics. We also accompany it with an example of planning algorithm, that enables the agent to solve RL tasks.