  AlphaZero has achieved impressive performance in deep reinforcement learning
by utilizing an architecture that combines search and training of a neural
network in self-play. Many researchers are looking for ways to reproduce and
improve results for other games/tasks. However, the architecture is designed to
learn from scratch, tabula rasa, accepting a cold-start problem in self-play.
Recently, a warm-start enhancement method for Monte Carlo Tree Search was
proposed to improve the self-play starting phase. It employs a fixed parameter
$I^\prime$ to control the warm-start length. Improved performance was reported
in small board games. In this paper we present results with an adaptive switch
method. Experiments show that our approach works better than the fixed
$I^\prime$, especially for "deep," tactical, games (Othello and Connect Four).
We conjecture that the adaptive value for $I^\prime$ is also influenced by the
size of the game, and that on average $I^\prime$ will increase with game size.
We conclude that AlphaZero-like deep reinforcement learning benefits from
adaptive rollout based warm-start, as Rapid Action Value Estimate did for
rollout-based reinforcement learning 15 years ago.
