  In this paper, we extend the Descent framework, which enables learning and
planning in the context of two-player games with perfect information, to the
framework of stochastic games.
  We propose two ways of doing this, the first way generalizes the search
algorithm, i.e. Descent, to stochastic games and the second way approximates
stochastic games by deterministic games.
  We then evaluate them on the game EinStein wurfelt nicht! against
state-of-the-art algorithms: Expectiminimax and Polygames (i.e. the Alpha Zero
algorithm). It is our generalization of Descent which obtains the best results.
The approximation by deterministic games nevertheless obtains good results,
presaging that it could give better results in particular contexts.
