Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimax Strikes Back   Deep Reinforcement Learning (DRL) reaches a superhuman level of play in many
complete information games. The state of the art search algorithm used in
combination with DRL is Monte Carlo Tree Search (MCTS). We take another
approach to DRL using a Minimax algorithm instead of MCTS and learning only the
evaluation of states, not the policy. We show that for multiple games it is
competitive with the state of the art DRL for the learning performances and for
the confrontations.

9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg