  Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes
a parameterized policy model for an expected return using gradient ascent.
Given a well-parameterized policy model, such as a neural network model, with
appropriate initial parameters, the PG algorithms work well even when
environment does not have the Markov property. Otherwise, they can be trapped
on a plateau or suffer from peakiness effects. As another successful RL
approach, algorithms based on Monte-Carlo Tree Search (MCTS), which include
AlphaZero, have obtained groundbreaking results especially on the board game
playing domain. They are also suitable to be applied to non-Markov decision
processes. However, since the standard MCTS does not have the ability to learn
state representation, the size of the tree-search space can be too large to
search. In this work, we examine a mixture policy of PG and MCTS to complement
each other's difficulties and take advantage of them. We derive conditions for
asymptotic convergence with results of a two-timescale stochastic approximation
and propose an algorithm that satisfies these conditions. The effectivity of
the proposed methods is verified through numerical experiments on non-Markov
decision processes.

Machine Learning;Artificial Intelligence