  In this paper, several techniques for learning game state evaluation
functions by reinforcement are proposed. The first is a generalization of tree
bootstrapping (tree learning): it is adapted to the context of reinforcement
learning without knowledge based on non-linear functions. With this technique,
no information is lost during the reinforcement learning process. The second is
a modification of minimax with unbounded depth extending the best sequences of
actions to the terminal states. This modified search is intended to be used
during the learning process. The third is to replace the classic gain of a game
(+1 / -1) with a reinforcement heuristic. We study particular reinforcement
heuristics such as: quick wins and slow defeats ; scoring ; mobility or
presence. The four is another variant of unbounded minimax, which plays the
safest action instead of playing the best action. This modified search is
intended to be used after the learning process. The five is a new action
selection distribution. The conducted experiments suggest that these techniques
improve the level of play. Finally, we apply these different techniques to
design program-players to the game of Hex (size 11 and 13) surpassing the level
of Mohex 3HNN with reinforcement learning from self-play without knowledge.
