  With the impact of real-time processing being realized in the recent past,
the need for efficient implementations of reinforcement learning algorithms has
been on the rise. Albeit the numerous advantages of Bellman equations utilized
in RL algorithms, they are not without the large search space of design
parameters.
  This research aims to shed light on the design space exploration associated
with reinforcement learning parameters, specifically that of Policy Iteration.
Given the large computational expenses of fine-tuning the parameters of
reinforcement learning algorithms, we propose an auto-tuner-based ordinal
regression approach to accelerate the process of exploring these parameters
and, in return, accelerate convergence towards an optimal policy. Our approach
provides 1.82x peak speedup with an average of 1.48x speedup over the previous
state-of-the-art.
