The module mainly deals with reading from and writing to TFRecords files, which is TensorFlow's native data-representation file format. When training AlphaGo Zero, we store MCTS self-play results and moves. As discussed, these then become the ground truths from which PolicyValueNetwork learns. TFRecords provides a convenient way to save historical moves and results from MCTS. When reading these from disk, turns TFRecords into tf.train.Example, an in-memory representation of data that can be directly fed into tf.estimator.Estimator.

 tf_records usually have filenames that end with *.tfrecord.zz.

The following function reads from a TFRecords file. We first turn a given list of TFRecords into ...

Get Python Reinforcement Learning Projects now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.