Train
idlePlay / inspect
your turnTrain a little, then challenge the network. The bars inside empty cells are the model's current move probabilities.
Training log
What this learner is allowed to know
Honest training contract
The model sees the board from the current player's perspective, chooses a legal move, then receives only the final outcome: +1 for win, 0 for draw, -1 for loss.
Adaptive reward is implemented as final_result - predicted_value. That means a draw can be good or bad depending on what the model expected, without any tactical hint being inserted.
Board positions are folded through the 8 tic-tac-toe symmetries. This reduces duplicate learning but does not tell the network how to play.
V5 change: the learner is unchanged from V4, but the game board is now a locked 3×3 square grid, AI move suggestions can be hidden during play, and the current model can be watched playing both sides against itself.
V4 learner: training is deliberately balanced between playing first and second. The learner explores under-tested actions early, but once a move has repeatedly performed badly in that exact canonical position, its exploration probability is reduced rather than endlessly repeated.