def _build_actions(self):
"""Create the chosen action with an exploration policy.
If inference mode is used the, actions are chosen directly without exploration.
"""
batch_size = get_tensor_batch_size(self._train_results.q)
exploration = self._build_exploration()
if self.is_continuous:
if exploration is None or Modes.is_infer(self.mode):
return self._train_results.q
# use exploration
return self._train_results.q + exploration
else:
self._index_action = tf.argmax(self._train_results.q, axis=1)
if exploration is None or Modes.is_infer(self.mode):
return self._index_action
# use exploration
exploration_size = tf.concat(axis=0, values=[
batch_size,
])
should_explore = tf.random_uniform((), 0, 1) < exploration
random_actions = tf.random_uniform(exploration_size, 0,
self.num_actions, tf.int64)
return tf.cond(should_explore, lambda: random_actions,
lambda: self._index_action)
def _build(self, features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results, features=features, labels=labels)
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(inputs=features)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features, features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results, features=features, labels=labels)
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
# We add 'useful' tensors to the graph collection so that we
# can easly find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
else:
losses, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features, features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results, features=features, labels=labels)
predictions = self._build_predictions(
results=results.results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, # pylint: disable=arguments-differ
features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
else:
_, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results, features=features, labels=labels)
predictions = self._build_predictions(results=results, features=features, labels=labels)
extra_ops = self._build_extra_ops(results=results, features=features, labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, incoming, loss_config, encoder_fn, decoder_fn, *args, **kwargs):
losses, loss = None, None
if Modes.GENERATE == self.mode:
results = self.decode(incoming=incoming, decoder_fn=decoder_fn)
elif Modes.ENCODE == self.mode:
results = self.encode(incoming=incoming, encoder_fn=encoder_fn)
else:
x = self.encode(incoming=incoming, encoder_fn=encoder_fn)
results = self.decode(incoming=x, decoder_fn=decoder_fn)
if not Modes.is_infer(self.mode):
losses, loss = self._build_loss(incoming, results, loss_config)
return BridgeSpec(results=results, losses=losses, loss=loss)
def _preprocess(self, features, labels):
"""Model specific preprocessing.
Args:
features: `array`, `Tensor` or `dict`. The environment states.
if `dict` it must contain a `state` key.
labels: `dict`. A dictionary containing `action`, `reward`, `advantage`.
"""
features, labels = super(TRPOModel, self)._preprocess(features, labels)
if not Modes.is_infer(self.mode) and 'dist_values' not in labels:
raise KeyError("labels must include the keys: `dist_values`.")
return features, labels
def _build(self, features, labels, loss_config, encoder_fn, decoder_fn, *args, **kwargs):
losses, loss = None, None
if Modes.GENERATE == self.mode:
results = self.decode(
incoming=features, features=features, labels=labels, decoder_fn=decoder_fn)
elif Modes.ENCODE == self.mode:
results = self.encode(features=features, labels=labels, encoder_fn=encoder_fn)
else:
x = self.encode(features=features, labels=labels, encoder_fn=encoder_fn)
results = self.decode(features=x, labels=labels, decoder_fn=decoder_fn)
if not Modes.is_infer(self.mode):
losses, loss = self._build_loss(results, features, labels, loss_config)
return BridgeSpec(results=results, losses=losses, loss=loss)
def _preprocess(self, features, labels):
"""Model specific preprocessing.
Args:
features: `array`, `Tensor` or `dict`. The environment states.
if `dict` it must contain a `state` key.
labels: `dict`. A dictionary containing `action`, `reward`, `advantage`.
"""
features, labels = super(BasePGModel, self)._preprocess(features, labels)
if not Modes.is_infer(self.mode) and 'discount_reward' not in labels:
raise KeyError("labels must include the keys: `discount_reward``.")
# TODO: add baseline here.
return features, labels
def _preprocess(self, features, labels):
"""Model specific preprocessing.
Args:
features: `array`, `Tensor` or `dict`. The environment states.
if `dict` it must contain a `state` key.
labels: `dict`. A dictionary containing `action`, `reward`, `advantage`.
"""
if isinstance(features, Mapping) and 'state' not in features:
raise KeyError("features must include a `state` key.")
if (not Modes.is_infer(self.mode) and
('action' not in labels or 'reward' not in labels or 'done' not in labels)):
raise KeyError("labels must include these keys: `action`, `reward`, `done`.")
return features, labels
def _preprocess(self, features, labels):
"""Model specific preprocessing.
Args:
features: `array`, `Tensor` or `dict`. The environment states.
if `dict` it must contain a `state` key.
labels: `dict`. A dictionary containing `action`, `reward`, `advantage`.
"""
if isinstance(features, Mapping) and 'state' not in features:
raise KeyError("features must include a `state` key.")
if (not Modes.is_infer(self.mode) and
('action' not in labels or 'reward' not in labels or 'done' not in labels)):
raise KeyError("labels must include these keys: `action`, `reward`, `done`.")
return features, labels
def _build_actions(self):
"""Create the chosen action with an exploration policy.
If inference mode is used the, actions are chosen directly without exploration.
"""
batch_size = get_tensor_batch_size(self._train_results.q)
exploration = self._build_exploration()
if self.is_continuous:
if exploration is None or Modes.is_infer(self.mode):
return self._train_results.q
# use exploration
return self._train_results.q + exploration
else:
self._index_action = tf.argmax(self._train_results.q, axis=1)
if exploration is None or Modes.is_infer(self.mode):
return self._index_action
# use exploration
exploration_size = tf.concat(axis=0, values=[batch_size, ])
should_explore = tf.random_uniform((), 0, 1) < exploration
random_actions = tf.random_uniform(exploration_size, 0, self.num_actions, tf.int64)
return tf.cond(should_explore, lambda: random_actions, lambda: self._index_action)
def _prepare_input_fn(self, mode, env):
"""Creates placeholders for the model given the mode and the env.
Args:
mode: Specifies if this training, evaluation or prediction. See `Modes`.
Returns:
`tuple`: (features, labels).
features: `dict`. {state: array}
labels: `dict`. {action: array, reward: array, done: array}
"""
if not isinstance(env, Environment):
raise TypeError("`env` must be an instance of `Environment`, "
"got `{}`".format(type(env)))
features = {'state': tf.placeholder(
dtype=tf.float32, shape=[None, env.num_states], name='state')}
if Modes.is_train(mode) or Modes.is_eval(mode):
return (
features,
{
'action': tf.placeholder(
dtype=tf.float32 if env.is_continuous else tf.int64,
shape=(None, env.num_actions) if env.is_continuous else (None,),
name='action'),
'reward': tf.placeholder(dtype=tf.float32, shape=(None,), name='reward'),
'discount_reward': tf.placeholder(dtype=tf.float32, shape=(None,), name='discount_reward'),
'done': tf.placeholder(dtype=tf.bool, shape=(None,), name='done'),
'dist_values': tf.placeholder(
dtype=tf.float32,
shape=(None, env.num_actions * 2) if env.is_continuous else (None, env.num_actions),
name='dist_values'),
'tangents': tf.placeholder(dtype=tf.float32, shape=(None,), name='tangents'),
'theta': tf.placeholder(dtype=tf.float32, shape=(None,), name='theta'),
'max_reward': tf.placeholder(
dtype=tf.float32, shape=(), name='max_reward'),
'min_reward': tf.placeholder(
dtype=tf.float32, shape=(), name='min_reward'),
'avg_reward': tf.placeholder(
dtype=tf.float32, shape=(), name='avg_reward'),
'total_reward': tf.placeholder(
dtype=tf.float32, shape=(), name='total_reward'),
}
)
if Modes.is_infer(mode):
return features, None
def _build_actions(self):
"""Create the chosen action w/o sampling.
If inference mode is used the, actions are chosen directly without sampling.
"""
batch_size = get_tensor_batch_size(self._graph_results.a)
if self.is_deterministic or Modes.is_infer(self.mode):
if self.is_continuous:
return self._graph_results.distribution.mean()
else:
return tf.argmax(self._graph_results.distribution.probs, axis=1)
else:
if self.is_continuous:
return self._graph_results.distribution.sample(sample_shape=batch_size)
else:
return tf.squeeze(self._graph_results.distribution.sample(sample_shape=batch_size), axis=1)
def _prepare_input_fn(self, mode, env):
"""Creates placeholders for the model given the mode and the env.
Args:
mode: Specifies if this training, evaluation or prediction. See `Modes`.
Returns:
`tuple`: (features, labels).
features: `dict`. {state: array}
labels: `dict`. {action: array, reward: array, done: array}
"""
if not isinstance(env, Environment):
raise TypeError("`env` must be an instance of `Environment`, "
"got `{}`".format(type(env)))
features = {
'state':
tf.placeholder(dtype=tf.float32,
shape=[None, env.num_states],
name='state')
}
if Modes.is_train(mode) or Modes.is_eval(mode):
return (features, {
'action':
tf.placeholder(
dtype=tf.float32 if env.is_continuous else tf.int64,
shape=(None, env.num_actions) if env.is_continuous else
(None, ),
name='action'),
'reward':
tf.placeholder(dtype=tf.float32, shape=(None, ),
name='reward'),
'done':
tf.placeholder(dtype=tf.bool, shape=(None, ), name='done'),
'max_reward':
tf.placeholder(dtype=tf.float32, shape=(), name='max_reward'),
'min_reward':
tf.placeholder(dtype=tf.float32, shape=(), name='min_reward'),
'avg_reward':
tf.placeholder(dtype=tf.float32, shape=(), name='avg_reward'),
'total_reward':
tf.placeholder(dtype=tf.float32, shape=(),
name='total_reward'),
})
if Modes.is_infer(mode):
return features, None
def _build(self, features, labels, params=None, config=None):
"""Build the different operation of the model."""
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(inputs=features)
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
extra_ops = self._build_extra_ops(results=results,
features=features,
labels=labels)
else:
losses, loss = self._build_loss(results, features, labels)
eval_metrics = self._build_eval_metrics(results, features, labels)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results,
features=features,
labels=labels)
predictions = self._build_predictions(results=results,
features=features,
labels=labels)
extra_ops = self._build_extra_ops(results=results,
features=features,
labels=labels)
# We add 'useful' tensors to the graph collection so that we
# can easily find them in our hooks/monitors.
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
extra_ops=extra_ops,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels=None, params=None, config=None):
# Pre-process features and labels
features, labels = self._preprocess(features, labels)
results = self._call_graph_fn(features=features, labels=labels)
if not isinstance(results, BridgeSpec):
raise ValueError('`bridge_fn` should return a BridgeSpec.')
loss = None
train_op = None
eval_metrics = None
if Modes.is_infer(self.mode):
predictions = self._build_predictions(results=results.results,
features=features,
labels=labels)
else:
_, loss = self._build_loss(results, features, features)
eval_metrics = self._build_eval_metrics(results.results, features,
features)
if Modes.is_train(self.mode):
train_op = self._build_train_op(loss)
self._build_summary_op(results=results.results,
features=features,
labels=labels)
predictions = self._build_predictions(results=results.results,
features=features,
labels=labels)
track(predictions, tf.GraphKeys.PREDICTIONS)
return EstimatorSpec(mode=self.mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
def _build(self, features, labels, loss, encoder_fn, decoder_fn, *args,
**kwargs):
losses, loss = None, None
if Modes.GENERATE == self.mode:
results = self.decode(incoming=features,
features=features,
labels=labels,
decoder_fn=decoder_fn)
elif Modes.ENCODE == self.mode:
results = self.encode(features=features,
labels=labels,
encoder_fn=encoder_fn)
else:
x = self.encode(features=features,
labels=labels,
encoder_fn=encoder_fn)
results = self.decode(features=x,
labels=labels,
decoder_fn=decoder_fn)
if not Modes.is_infer(self.mode):
losses, loss = self._build_loss(results, features, labels,
loss)
return BridgeSpec(results=results, losses=losses, loss=loss)
