This repo was created to provide a high performance Tensorflow Agents environment for reinforcement learning.

Run random_benchmark.random_benchmark() to find the mean and standard deviation of the number of turns in games completed by random policies.

Run mapped_train_eval.mapped_train_eval() to train and eval the polices.

The provided notebook in tf.pytan.ipynb will complete the above tasks.