e4a3b3fe38
example: python -m pybullet_envs.agents.train_ppo --config=pybullet_pendulum --logdir=pendulum
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
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r"""Script to visualize the trained PPO agent.
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python -m pybullet_envs.agents.visualize \
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--logdir=ppo
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--outdir=/tmp/video/
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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 tensorflow as tf
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from agents.scripts import visualize
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flags = tf.app.flags
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FLAGS = tf.app.flags.FLAGS
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flags.DEFINE_string("logdir", None,
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"Directory to the checkpoint of a training run.")
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flags.DEFINE_string("outdir", None,
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"Local directory for storing the monitoring outdir.")
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flags.DEFINE_string("checkpoint", None,
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"Checkpoint name to load; defaults to most recent.")
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flags.DEFINE_integer("num_agents", 1,
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"How many environments to step in parallel.")
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flags.DEFINE_integer("num_episodes", 1, "Minimum number of episodes to render.")
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flags.DEFINE_boolean(
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"env_processes", False,
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"Step environments in separate processes to circumvent the GIL.")
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def main(_):
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visualize.visualize(FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents,
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FLAGS.num_episodes, FLAGS.checkpoint, FLAGS.env_processes)
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if __name__ == "__main__":
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tf.app.run()
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