  Recent breakthroughs in Artificial Intelligence have shown that the
combination of tree-based planning with deep learning can lead to superior
performance. We present Adaptive Entropy Tree Search (ANTS) - a novel algorithm
combining planning and learning in the maximum entropy paradigm. Through a
comprehensive suite of experiments on the Atari benchmark we show that ANTS
significantly outperforms PUCT, the planning component of the state-of-the-art
AlphaZero system. ANTS builds upon recent work on maximum entropy planning
methods - which however, as we show, fail in combination with learning. ANTS
resolves this issue to reach state-of-the-art performance. We further find that
ANTS exhibits superior robustness to different hyperparameter choices, compared
to the previous algorithms. We believe that the high performance and robustness
of ANTS can bring tree search planning one step closer to wide practical
adoption.
