Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Monte-Carlo Tree Search as Regularized Policy Optimization   The combination of Monte-Carlo tree search (MCTS) with deep reinforcement
learning has led to significant advances in artificial intelligence. However,
AlphaZero, the current state-of-the-art MCTS algorithm, still relies on
handcrafted heuristics that are only partially understood. In this paper, we
show that AlphaZero's search heuristics, along with other common ones such as
UCT, are an approximation to the solution of a specific regularized policy
optimization problem. With this insight, we propose a variant of AlphaZero
which uses the exact solution to this policy optimization problem, and show
experimentally that it reliably outperforms the original algorithm in multiple
domains.

10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat