./train.py --model_dir /tmp/a3c --env Breakout-v0 --t_max 5 --eval_every 300 --parallelism 8
See ./train.py --help for a full list of options. Then, monitor training progress in Tensorboard:
tensorboard --logdir=/tmp/a3c
train.py contains the main method to start training.estimators.py contains the Tensorflow graph definitions for the Policy and Value networks.worker.py contains code that runs in each worker threads.policy_monitor.py contains code that evaluates the policy network by running an episode and saving rewards to Tensorboard.