This question has come from my experiment of building a cnn based tic-tac-toe game that I'm using as a beginner machine learning project. The game works purely on policy networks, more specifically -During training, at the end of each game, it trains itself on the moves the winner/drawer made for each board position. That is, its training data consists of board positions and the moves made by the winning player on each position.While playing, it predicts its own moves solely based on that training (that is, it predicts what move would a winning player make with the current board). It doesn't use any type of search or value networks.I'm seeing that if I train it against a player that predicts the perfect move (using a recursive search) every time, the AI gets good at drawing about 50% games. But if I train it against a player that makes random moves, it doesn't get better at all.Wouldn't one expect it to learn well (even if slower) regardless of the level of its opponent? Since each game ends in a draw or win for one player, shouldn't it be able to extract features for the winning/drawing strategies even when learning from random players? Or does this behavior mean that the model is not optimal?
Can a purely policy convolution neural network based game learn to play better than its opponents?
convolutional neural networks