def _build_model(self, model_dir):
"""Construct a KerasModel containing the policy and loss calculations."""
state_shape = self._env.state_shape
state_dtype = self._env.state_dtype
if not self._state_is_list:
state_shape = [state_shape]
state_dtype = [state_dtype]
features = []
for s, d in zip(state_shape, state_dtype):
features.append(
tf.keras.layers.Input(shape=list(s), dtype=tf.as_dtype(d)))
policy_model = self._policy.create_model()
output_names = self._policy.output_names
loss = PPOLoss(self.value_weight, self.entropy_weight,
self.clipping_width, output_names.index('action_prob'),
output_names.index('value'))
model = KerasModel(policy_model,
loss,
batch_size=self.max_rollout_length,
model_dir=model_dir,
optimize=self._optimizer)
env = self._env
example_inputs = [
np.zeros([model.batch_size] + list(shape), dtype)
for shape, dtype in zip(state_shape, state_dtype)
]
example_labels = [np.zeros((model.batch_size, env.n_actions))]
example_weights = [np.zeros(model.batch_size)] * 3
model._create_training_ops(
(example_inputs, example_labels, example_weights))
return model
def _build_model(self, model_dir):
"""Construct a KerasModel containing the policy and loss calculations."""
policy_model = self._policy.create_model()
loss = PPOLoss(self.value_weight, self.entropy_weight,
self.clipping_width, self._action_prob_index,
self._value_index)
model = KerasModel(policy_model,
loss,
batch_size=self.max_rollout_length,
model_dir=model_dir,
optimize=self._optimizer)
model._ensure_built()
return model
def _build_model(self, model_dir):
"""Construct a KerasModel containing the policy and loss calculations."""
policy_model = self._policy.create_model()
if self.continuous:
loss = A2CLossContinuous(self.value_weight, self.entropy_weight,
self._action_mean_index,
self._action_std_index, self._value_index)
else:
loss = A2CLossDiscrete(self.value_weight, self.entropy_weight,
self._action_prob_index, self._value_index)
model = KerasModel(policy_model,
loss,
batch_size=self.max_rollout_length,
model_dir=model_dir,
optimize=self._optimizer)
model._ensure_built()
return model