  In this paper, we present the Adaptive EntropyTree Search (ANTS) algorithm.
ANTS builds on recent successes of maximum entropy planning while mitigating
its arguably major drawback - sensitivity to the temperature setting. We endow
ANTS with a mechanism, which adapts the temperature to match a given range of
action selection entropy in the nodes of the planning tree. With this
mechanism, the ANTS planner enjoys remarkable hyper-parameter robustness,
achieves high scores on the Atari benchmark, and is a capable component of a
planning-learning loop akin to AlphaZero. We believe that all these features
make ANTS a compelling choice for a general planner for complex tasks.
