  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.

Machine Learning;Artificial Intelligence;Machine Learning