e4a3b3fe38
example: python -m pybullet_envs.agents.train_ppo --config=pybullet_pendulum --logdir=pendulum
49 lines
1.2 KiB
Python
49 lines
1.2 KiB
Python
r"""Script to use Proximal Policy Gradient for the minitaur environments.
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Run:
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python train_ppo.py --logdif=/tmp/train --config=minitaur_pybullet
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import datetime
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import os
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import tensorflow as tf
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from agents import tools
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from agents.scripts import train
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from agents.scripts import utility
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from . import config_ppo
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flags = tf.app.flags
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FLAGS = tf.app.flags.FLAGS
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flags.DEFINE_string(
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'logdir', None,
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'Base directory to store logs.')
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flags.DEFINE_string(
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'config', None,
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'Configuration to execute.')
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flags.DEFINE_string(
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'timestamp', datetime.datetime.now().strftime('%Y%m%dT%H%M%S'),
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'Sub directory to store logs.')
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def main(_):
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"""Create or load configuration and launch the trainer."""
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config = tools.AttrDict(getattr(config_ppo, FLAGS.config)())
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logdir = FLAGS.logdir and os.path.join(
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FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config))
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utility.save_config(config, logdir)
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for score in train.train(config, env_processes=True):
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tf.logging.info(str(score))
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if __name__ == '__main__':
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tf.app.run()
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