SugaR uses the MCTS option (MonteCarloTreeSearch), it is activated by default, the motor behavior is similar to the AlphaZero concepts.
creates three files for machine learning purposes:
-experience.bin when no more than 40 moves are played, there are non more than 6 pieces on the chessboard and at a not low depth in analysis
-pawngame.bin
when there are no more than 2 pieces and the game's phase is not the ending
-openings.bin, in the form .bin (>=1) at the initial stage of game with memorized the move played, the depth and the score. In this mode, the engine is not less strong than Stockfish in a match play without learning, but a lot better in analysis mode and to solve hard positions. With learning, the engine became stronger and stronger. The default mode, conversely, is stronger than Stockfish in a match play, but not as good as MCTS for the rest and can't improve its play because of no learning enabled.
Example:


Dynamic Strategy
To be used as additional support in the analysis of particularly complex positions. With the increase of the score, that is how much the motor is in advantage or fundamentally closer to the checkmate. Or In all favorable pressing situations; The advanced Pawns are penalized and the King gains more importance because we must pay attention to the compactness and the other way around.

Regards
Marco