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/visualize_ppo.py

@@ -0,0 +1,42 @@
+
+r"""Script to visualize the trained PPO agent.
+
+python -m pybullet_envs.agents.visualize \
+--logdir=ppo
+--outdir=/tmp/video/
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from agents.scripts import visualize
+
+
+flags = tf.app.flags
+FLAGS = tf.app.flags.FLAGS
+flags.DEFINE_string("logdir", None,
+                    "Directory to the checkpoint of a training run.")
+flags.DEFINE_string("outdir", None,
+                    "Local directory for storing the monitoring outdir.")
+flags.DEFINE_string("checkpoint", None,
+                    "Checkpoint name to load; defaults to most recent.")
+flags.DEFINE_integer("num_agents", 1,
+                     "How many environments to step in parallel.")
+flags.DEFINE_integer("num_episodes", 1, "Minimum number of episodes to render.")
+flags.DEFINE_boolean(
+    "env_processes", False,
+    "Step environments in separate processes to circumvent the GIL.")
+
+
+def main(_):
+  visualize.visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents,
+                      FLAGS.num_episodes, FLAGS.checkpoint, FLAGS.env_processes)
+
+
+if __name__ == "__main__":
+  tf.app.run()
+