HTMRL: Biologically Plausible Reinforcement Learning with Hierarchical Temporal Memory

TL;DR

HTMRL introduces a biologically plausible reinforcement learning algorithm based on Hierarchical Temporal Memory, demonstrating strong adaptability to non-stationary environments and potential for meta-reinforcement learning.

Contribution

This paper presents the first HTM-based RL algorithm, showing its scalability and ability to adapt to changing patterns in non-stationary environments.

Findings

Performs well on a 10-armed bandit after 750 steps

Adapts quickly to sudden changes in the environment

Scales to many states and actions

Abstract

Building Reinforcement Learning (RL) algorithms which are able to adapt to continuously evolving tasks is an open research challenge. One technology that is known to inherently handle such non-stationary input patterns well is Hierarchical Temporal Memory (HTM), a general and biologically plausible computational model for the human neocortex. As the RL paradigm is inspired by human learning, HTM is a natural framework for an RL algorithm supporting non-stationary environments. In this paper, we present HTMRL, the first strictly HTM-based RL algorithm. We empirically and statistically show that HTMRL scales to many states and actions, and demonstrate that HTM's ability for adapting to changing patterns extends to RL. Specifically, HTMRL performs well on a 10-armed bandit after 750 steps, but only needs a third of that to adapt to the bandit suddenly shuffling its arms. HTMRL is the first iteration of a novel RL approach, with the potential of extending to a capable algorithm for Meta-RL.