Change network interface from RNNCell to function.

README.md

@@ -68,7 +68,7 @@ modifying the code:
 | File | Content |
 | ---- | ------- |
 | `scripts/configs.py` | Experiment configurations specifying the tasks and algorithms. |
-| `scripts/networks.py` | Neural network models defined as [TensorFlow RNNCells][tf-rnn-cell]. |
+| `scripts/networks.py` | Neural network models. |
 | `scripts/train.py` | The executable file containing the training setup. |
 | `ppo/algorithm.py` | The TensorFlow graph for the PPO algorithm. |
 
@@ -80,8 +80,6 @@ python3 -m unittest discover -p "*_test.py"
 
 For further questions, please open an issue on Github.
 
-[tf-rnn-cell]: https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/RNNCell
-
 Implementation
 --------------
 

agents/ppo/algorithm.py

@@ -23,6 +23,7 @@
 from __future__ import print_function
 
 import collections
+import functools
 
 import tensorflow as tf
 
@@ -31,10 +32,6 @@
 from agents.ppo import utility
 
 
-_NetworkOutput = collections.namedtuple(
-    'NetworkOutput', 'policy, mean, logstd, value, state')
-
-
 class PPOAlgorithm(object):
   """A vectorized implementation of the PPO algorithm by John Schulman."""
 
@@ -70,15 +67,25 @@ def __init__(self, batch_env, step, is_training, should_log, config):
     use_gpu = self._config.use_gpu and utility.available_gpus()
     with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
       # Create network variables for later calls to reuse.
-      self._network(
+      action_size = self._batch_env.action.shape[1].value
+      self._network = tf.make_template(
+          'network', functools.partial(config.network, config, action_size))
+      output = self._network(
           tf.zeros_like(self._batch_env.observ)[:, None],
-          tf.ones(len(self._batch_env)), reuse=None)
-      cell = self._config.network(self._batch_env.action.shape[1].value)
+          tf.ones(len(self._batch_env)))
       with tf.variable_scope('ppo_temporary'):
         self._episodes = memory.EpisodeMemory(
             template, len(batch_env), config.max_length, 'episodes')
-        self._last_state = utility.create_nested_vars(
-            cell.zero_state(len(batch_env), tf.float32))
+        if output.state is None:
+          self._last_state = None
+        else:
+          # Ensure the batch dimension is set.
+          tf.contrib.framework.nest.map_structure(
+              lambda x: x.set_shape([len(batch_env)] + x.shape.as_list()[1:]),
+              output.state)
+          self._last_state = tf.contrib.framework.nest.map_structure(
+              lambda x: tf.Variable(lambda: tf.zeros_like(x), False),
+              output.state)
         self._last_action = tf.Variable(
             tf.zeros_like(self._batch_env.action), False, name='last_action')
         self._last_mean = tf.Variable(
@@ -102,7 +109,10 @@ def begin_episode(self, agent_indices):
       Summary tensor.
     """
     with tf.name_scope('begin_episode/'):
-      reset_state = utility.reinit_nested_vars(self._last_state, agent_indices)
+      if self._last_state is None:
+        reset_state = tf.no_op()
+      else:
+        reset_state = utility.reinit_nested_vars(self._last_state, agent_indices)
       reset_buffer = self._episodes.clear(agent_indices)
       with tf.control_dependencies([reset_state, reset_buffer]):
         return tf.constant('')
@@ -130,8 +140,12 @@ def perform(self, observ):
           tf.summary.histogram('action', action[:, 0]),
           tf.summary.histogram('logprob', logprob)]), str)
       # Remember current policy to append to memory in the experience callback.
+      if self._last_state is None:
+        assign_state = tf.no_op()
+      else:
+        assign_state = utility.assign_nested_vars(self._last_state, network.state)
       with tf.control_dependencies([
-          utility.assign_nested_vars(self._last_state, network.state),
+          assign_state,
           self._last_action.assign(action[:, 0]),
           self._last_mean.assign(network.mean[:, 0]),
           self._last_logstd.assign(network.logstd[:, 0])]):
@@ -523,36 +537,3 @@ def _mask(self, tensor, length):
       mask = tf.cast(range_[None, :] < length[:, None], tf.float32)
       masked = tensor * mask
       return tf.check_numerics(masked, 'masked')
-
-  def _network(self, observ, length=None, state=None, reuse=True):
-    """Compute the network output for a batched sequence of observations.
-
-    Optionally, the initial state can be specified. The weights should be
-    reused for all calls, except for the first one. Output is a named tuple
-    containing the policy as a TensorFlow distribution, the policy mean and log
-    standard deviation, the approximated state value, and the new recurrent
-    state.
-
-    Args:
-      observ: Sequences of observations.
-      length: Batch of sequence lengths.
-      state: Batch of initial recurrent states.
-      reuse: Python boolean whether to reuse previous variables.
-
-    Returns:
-      NetworkOutput tuple.
-    """
-    with tf.variable_scope('network', reuse=reuse):
-      observ = tf.convert_to_tensor(observ)
-      use_gpu = self._config.use_gpu and utility.available_gpus()
-      with tf.device('/gpu:0' if use_gpu else '/cpu:0'):
-        observ = tf.check_numerics(observ, 'observ')
-        cell = self._config.network(self._batch_env.action.shape[1].value)
-        (mean, logstd, value), state = tf.nn.dynamic_rnn(
-            cell, observ, length, state, tf.float32, swap_memory=True)
-      mean = tf.check_numerics(mean, 'mean')
-      logstd = tf.check_numerics(logstd, 'logstd')
-      value = tf.check_numerics(value, 'value')
-      policy = tf.contrib.distributions.MultivariateNormalDiag(
-          mean, tf.exp(logstd))
-      return _NetworkOutput(policy, mean, logstd, value, state)

agents/ppo/utility.py

@@ -26,20 +26,6 @@
 from tensorflow.python.client import device_lib
 
 
-def create_nested_vars(tensors):
-  """Create variables matching a nested tuple of tensors.
-
-  Args:
-    tensors: Nested tuple of list of tensors.
-
-  Returns:
-    Nested tuple or list of variables.
-  """
-  if isinstance(tensors, (tuple, list)):
-    return type(tensors)(create_nested_vars(tensor) for tensor in tensors)
-  return tf.Variable(tensors, False)
-
-
 def reinit_nested_vars(variables, indices=None):
   """Reset all variables in a nested tuple to zeros.
 

agents/scripts/configs.py

@@ -32,11 +32,9 @@ def default():
   eval_episodes = 25
   use_gpu = False
   # Network
-  network = networks.ForwardGaussianPolicy
+  network = networks.feed_forward_gaussian
   weight_summaries = dict(
-      all=r'.*',
-      policy=r'.*/policy/.*',
-      value=r'.*/value/.*')
+      all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*')
   policy_layers = 200, 100
   value_layers = 200, 100
   init_mean_factor = 0.05

agents/scripts/networks.py

@@ -12,109 +12,114 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Networks for the PPO algorithm defined as recurrent cells."""
+"""Network definitions for the PPO algorithm."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
+import functools
+import operator
+
 import tensorflow as tf
 
 
-_MEAN_WEIGHTS_INITIALIZER = tf.contrib.layers.variance_scaling_initializer(
-    factor=0.1)
-_LOGSTD_INITIALIZER = tf.random_normal_initializer(-1, 1e-10)
+NetworkOutput = collections.namedtuple(
+    'NetworkOutput', 'policy, mean, logstd, value, state')
 
 
-class ForwardGaussianPolicy(tf.contrib.rnn.RNNCell):
+def feed_forward_gaussian(
+    config, action_size, observations, length, state=None):
   """Independent feed forward networks for policy and value.
 
   The policy network outputs the mean action and the log standard deviation
   is learned as independent parameter vector.
-  """
 
-  def __init__(
-      self, policy_layers, value_layers, action_size,
-      mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER,
-      logstd_initializer=_LOGSTD_INITIALIZER):
-    self._policy_layers = policy_layers
-    self._value_layers = value_layers
-    self._action_size = action_size
-    self._mean_weights_initializer = mean_weights_initializer
-    self._logstd_initializer = logstd_initializer
-
-  @property
-  def state_size(self):
-    unused_state_size = 1
-    return unused_state_size
-
-  @property
-  def output_size(self):
-    return (self._action_size, self._action_size, tf.TensorShape([]))
-
-  def __call__(self, observation, state):
-    with tf.variable_scope('policy'):
-      x = tf.contrib.layers.flatten(observation)
-      for size in self._policy_layers:
-        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-      mean = tf.contrib.layers.fully_connected(
-          x, self._action_size, tf.tanh,
-          weights_initializer=self._mean_weights_initializer)
-      logstd = tf.get_variable(
-          'logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
-      logstd = tf.tile(
-          logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
-    with tf.variable_scope('value'):
-      x = tf.contrib.layers.flatten(observation)
-      for size in self._value_layers:
-        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-      value = tf.contrib.layers.fully_connected(x, 1, None)[:, 0]
-    return (mean, logstd, value), state
-
-
-class RecurrentGaussianPolicy(tf.contrib.rnn.RNNCell):
+  Args:
+    config: Configuration object.
+    action_size: Length of the action vector.
+    observations: Sequences of observations.
+    length: Batch of sequence lengths.
+    state: Batch of initial recurrent states.
+
+  Returns:
+    NetworkOutput tuple.
+  """
+  mean_weights_initializer = tf.contrib.layers.variance_scaling_initializer(
+      factor=config.init_mean_factor)
+  logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
+  flat_observations = tf.reshape(observations, [
+      tf.shape(observations)[0], tf.shape(observations)[1],
+      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
+  with tf.variable_scope('policy'):
+    x = flat_observations
+    for size in config.policy_layers:
+      x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
+    mean = tf.contrib.layers.fully_connected(
+        x, action_size, tf.tanh,
+        weights_initializer=mean_weights_initializer)
+    logstd = tf.tile(tf.get_variable(
+        'logstd', mean.shape[2:], tf.float32, logstd_initializer)[None, None],
+        [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
+  with tf.variable_scope('value'):
+    x = flat_observations
+    for size in config.value_layers:
+      x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
+    value = tf.contrib.layers.fully_connected(x, 1, None)[:, 0]
+  mean = tf.check_numerics(mean, 'mean')
+  logstd = tf.check_numerics(logstd, 'logstd')
+  value = tf.check_numerics(value, 'value')
+  policy = tf.contrib.distributions.MultivariateNormalDiag(
+      mean, tf.exp(logstd))
+  return NetworkOutput(policy, mean, logstd, value, state)
+
+
+def recurrent_gaussian(
+    config, action_size, observations, length, state=None):
   """Independent recurrent policy and feed forward value networks.
 
   The policy network outputs the mean action and the log standard deviation
   is learned as independent parameter vector. The last policy layer is recurrent
   and uses a GRU cell.
-  """
 
-  def __init__(
-      self, policy_layers, value_layers, action_size,
-      mean_weights_initializer=_MEAN_WEIGHTS_INITIALIZER,
-      logstd_initializer=_LOGSTD_INITIALIZER):
-    self._policy_layers = policy_layers
-    self._value_layers = value_layers
-    self._action_size = action_size
-    self._mean_weights_initializer = mean_weights_initializer
-    self._logstd_initializer = logstd_initializer
-    self._cell = tf.contrib.rnn.GRUBlockCell(100)
-
-  @property
-  def state_size(self):
-    return self._cell.state_size
-
-  @property
-  def output_size(self):
-    return (self._action_size, self._action_size, tf.TensorShape([]))
-
-  def __call__(self, observation, state):
-    with tf.variable_scope('policy'):
-      x = tf.contrib.layers.flatten(observation)
-      for size in self._policy_layers[:-1]:
-        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-      x, state = self._cell(x, state)
-      mean = tf.contrib.layers.fully_connected(
-          x, self._action_size, tf.tanh,
-          weights_initializer=self._mean_weights_initializer)
-      logstd = tf.get_variable(
-          'logstd', mean.shape[1:], tf.float32, self._logstd_initializer)
-      logstd = tf.tile(
-          logstd[None, ...], [tf.shape(mean)[0]] + [1] * logstd.shape.ndims)
-    with tf.variable_scope('value'):
-      x = tf.contrib.layers.flatten(observation)
-      for size in self._value_layers:
-        x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
-      value = tf.contrib.layers.fully_connected(x, 1, None)[:, 0]
-    return (mean, logstd, value), state
+  Args:
+    config: Configuration object.
+    action_size: Length of the action vector.
+    observations: Sequences of observations.
+    length: Batch of sequence lengths.
+    state: Batch of initial recurrent states.
+
+  Returns:
+    NetworkOutput tuple.
+  """
+  mean_weights_initializer = tf.contrib.layers.variance_scaling_initializer(
+      factor=config.init_mean_factor)
+  logstd_initializer = tf.random_normal_initializer(config.init_logstd, 1e-10)
+  cell = tf.contrib.rnn.GRUBlockCell(config.policy_layers[-1])
+  flat_observations = tf.reshape(observations, [
+      tf.shape(observations)[0], tf.shape(observations)[1],
+      functools.reduce(operator.mul, observations.shape.as_list()[2:], 1)])
+  with tf.variable_scope('policy'):
+    x = flat_observations
+    for size in config.policy_layers[:-1]:
+      x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
+    x, state = tf.nn.dynamic_rnn(cell, x, length, state, tf.float32)
+    mean = tf.contrib.layers.fully_connected(
+        x, action_size, tf.tanh,
+        weights_initializer=mean_weights_initializer)
+    logstd = tf.tile(tf.get_variable(
+        'logstd', mean.shape[2:], tf.float32, logstd_initializer)[None, None],
+        [tf.shape(mean)[0], tf.shape(mean)[1]] + [1] * (mean.shape.ndims - 2))
+  with tf.variable_scope('value'):
+    x = flat_observations
+    for size in config.value_layers:
+      x = tf.contrib.layers.fully_connected(x, size, tf.nn.relu)
+    value = tf.contrib.layers.fully_connected(x, 1, None)[:, 0]
+  mean = tf.check_numerics(mean, 'mean')
+  logstd = tf.check_numerics(logstd, 'logstd')
+  value = tf.check_numerics(value, 'value')
+  policy = tf.contrib.distributions.MultivariateNormalDiag(
+      mean, tf.exp(logstd))
+  # assert state.shape.as_list()[0] is not None
+  return NetworkOutput(policy, mean, logstd, value, state)

agents/scripts/train.py

@@ -101,8 +101,6 @@ def train(config, env_processes):
   """
   tf.reset_default_graph()
   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)
   if config.update_every % config.num_agents: