  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.
