make sure that the pre-trained galloping and trotting policies work for the minitaur_reactive_env and minitaur_trotting_env environments.

examples/pybullet/gym/pybullet_envs/minitaur/agents/scripts/visualize.py

@@ -0,0 +1,157 @@
+# Copyright 2017 The TensorFlow Agents Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+r"""Script to render videos of the Proximal Policy Gradient algorithm.
+
+Command line:
+
+  python3 -m agents.scripts.visualize \
+      --logdir=/path/to/logdir/<time>-<config> --outdir=/path/to/outdir/
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import os
+
+import gym
+import tensorflow as tf
+
+from pybullet_envs.minitaur.agents import tools
+from pybullet_envs.minitaur.agents.scripts import utility
+
+
+def _create_environment(config, outdir):
+  """Constructor for an instance of the environment.
+
+  Args:
+    config: Object providing configurations via attributes.
+    outdir: Directory to store videos in.
+
+  Returns:
+    Wrapped OpenAI Gym environment.
+  """
+  if isinstance(config.env, str):
+    env = gym.make(config.env)
+  else:
+    env = config.env()
+  # Ensure that the environment has the specification attribute set as expected
+  # by the monitor wrapper.
+  if not hasattr(env, 'spec'):
+    setattr(env, 'spec', getattr(env, 'spec', None))
+  if config.max_length:
+    env = tools.wrappers.LimitDuration(env, config.max_length)
+#  env = gym.wrappers.Monitor(
+#      env, outdir, lambda unused_episode_number: True)
+  env = tools.wrappers.RangeNormalize(env)
+  env = tools.wrappers.ClipAction(env)
+  env = tools.wrappers.ConvertTo32Bit(env)
+  return env
+
+
+def _define_loop(graph, eval_steps):
+  """Create and configure an evaluation loop.
+
+  Args:
+    graph: Object providing graph elements via attributes.
+    eval_steps: Number of evaluation steps per epoch.
+
+  Returns:
+    Loop object.
+  """
+  loop = tools.Loop(
+      None, graph.step, graph.should_log, graph.do_report, graph.force_reset)
+  loop.add_phase(
+      'eval', graph.done, graph.score, graph.summary, eval_steps,
+      report_every=eval_steps,
+      log_every=None,
+      checkpoint_every=None,
+      feed={graph.is_training: False})
+  return loop
+
+
+def visualize(
+    logdir, outdir, num_agents, num_episodes, checkpoint=None,
+    env_processes=True):
+  """Recover checkpoint and render videos from it.
+
+  Args:
+    logdir: Logging directory of the trained algorithm.
+    outdir: Directory to store rendered videos in.
+    num_agents: Number of environments to simulate in parallel.
+    num_episodes: Total number of episodes to simulate.
+    checkpoint: Checkpoint name to load; defaults to most recent.
+    env_processes: Whether to step environments in separate processes.
+  """
+  config = utility.load_config(logdir)
+  with config.unlocked:
+    config.network = functools.partial(
+        utility.define_network, config.network, config)
+    config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
+    config.value_optimizer = getattr(tf.train, config.value_optimizer)
+  with tf.device('/cpu:0'):
+    batch_env = utility.define_batch_env(
+        lambda: _create_environment(config, outdir),
+        num_agents, env_processes)
+    graph = utility.define_simulation_graph(
+        batch_env, config.algorithm, config)
+    total_steps = num_episodes * config.max_length
+    loop = _define_loop(graph, total_steps)
+  saver = utility.define_saver(
+      exclude=(r'.*_temporary/.*', r'global_step'))
+  sess_config = tf.ConfigProto(allow_soft_placement=True)
+  sess_config.gpu_options.allow_growth = True
+  with tf.Session(config=sess_config) as sess:
+    utility.initialize_variables(
+        sess, saver, config.logdir, checkpoint, resume=True)
+    for unused_score in loop.run(sess, saver, total_steps):
+      pass
+  batch_env.close()
+
+
+def main(_):
+  """Load a trained algorithm and render videos."""
+  utility.set_up_logging()
+  if not FLAGS.logdir or not FLAGS.outdir:
+    raise KeyError('You must specify logging and outdirs directories.')
+  FLAGS.logdir = os.path.expanduser(FLAGS.logdir)
+  FLAGS.outdir = os.path.expanduser(FLAGS.outdir)
+  visualize(
+      FLAGS.logdir, FLAGS.outdir, FLAGS.num_agents, FLAGS.num_episodes,
+      FLAGS.checkpoint, FLAGS.env_processes)
+
+
+if __name__ == '__main__':
+  FLAGS = tf.app.flags.FLAGS
+  tf.app.flags.DEFINE_string(
+      'logdir', None,
+      'Directory to the checkpoint of a training run.')
+  tf.app.flags.DEFINE_string(
+      'outdir', None,
+      'Local directory for storing the monitoring outdir.')
+  tf.app.flags.DEFINE_string(
+      'checkpoint', None,
+      'Checkpoint name to load; defaults to most recent.')
+  tf.app.flags.DEFINE_integer(
+      'num_agents', 1,
+      'How many environments to step in parallel.')
+  tf.app.flags.DEFINE_integer(
+      'num_episodes', 5,
+      'Minimum number of episodes to render.')
+  tf.app.flags.DEFINE_boolean(
+      'env_processes', True,
+      'Step environments in separate processes to circumvent the GIL.')
+  tf.app.run()