add TensorFlow Agents PPO training script for various pybullet environments:

example:

python -m pybullet_envs.agents.train_ppo --config=pybullet_pendulum --logdir=pendulum

examples/pybullet/gym/pybullet_envs/agents/train_ppo.py

@@ -0,0 +1,48 @@
+r"""Script to use Proximal Policy Gradient for the minitaur environments.
+
+Run:
+ python train_ppo.py --logdif=/tmp/train --config=minitaur_pybullet
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import datetime
+import os
+import tensorflow as tf
+
+from agents import tools
+from agents.scripts import train
+from agents.scripts import utility
+from . import config_ppo
+
+
+flags = tf.app.flags
+FLAGS = tf.app.flags.FLAGS
+
+flags.DEFINE_string(
+    'logdir', None,
+    'Base directory to store logs.')
+flags.DEFINE_string(
+    'config', None,
+    'Configuration to execute.')
+flags.DEFINE_string(
+    'timestamp', datetime.datetime.now().strftime('%Y%m%dT%H%M%S'),
+    'Sub directory to store logs.')
+
+
+def main(_):
+  """Create or load configuration and launch the trainer."""
+  config = tools.AttrDict(getattr(config_ppo, FLAGS.config)())
+  logdir = FLAGS.logdir and os.path.join(
+      FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config))
+  utility.save_config(config, logdir)
+  for score in train.train(config, env_processes=True):
+    tf.logging.info(str(score))
+
+
+if __name__ == '__main__':
+  tf.app.run()
+