def test_ppo_model_cc_visual(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=2)
env = UnityEnvironment(' ')
model = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output, model.log_probs, model.value, model.entropy,
model.learning_rate
]
feed_dict = {
model.batch_size: 2,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.visual_in[0]: np.ones([2, 40, 30, 3]),
model.visual_in[1]: np.ones([2, 40, 30, 3]),
model.epsilon: np.array([[0, 1], [2, 3]])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def test_ppo_model_cc_vector_rnn(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
memory_size = 128
model = PPOModel(env.brains["RealFakeBrain"],
use_recurrent=True,
m_size=memory_size)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output, model.all_log_probs, model.value, model.entropy,
model.learning_rate, model.memory_out
]
feed_dict = {
model.batch_size: 1,
model.sequence_length: 2,
model.memory_in: np.zeros((1, memory_size)),
model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.epsilon: np.array([[0, 1]])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def test_ppo_model_dc_vector_curio(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=0)
env = UnityEnvironment(' ')
model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output, model.all_log_probs, model.value, model.entropy,
model.learning_rate, model.intrinsic_reward
]
feed_dict = {
model.batch_size:
2,
model.sequence_length:
1,
model.vector_in:
np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.next_vector_in:
np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.action_holder: [[0], [0]],
model.action_masks:
np.ones([2, 2])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def __init__(self, seed, brain, trainer_params, is_training, load,
use_depth, save_activations):
"""
Policy for Proximal Policy Optimization Networks.
:param seed: Random seed.
:param brain: Assigned Brain object.
:param trainer_params: Defined training parameters.
:param is_training: Whether the model should be trained.
:param load: Whether a pre-trained model will be loaded or a new one created.
:param use_depth: Augment visual input with depth information.
:param save_activations: Save network activations.
"""
super().__init__(seed, brain, trainer_params)
self.has_updated = False
self.use_curiosity = bool(trainer_params['use_curiosity'])
self.save_activations = save_activations
self.allGinis = []
self.VisGinis = []
with self.graph.as_default():
self.model = PPOModel(
brain,
lr=float(trainer_params['learning_rate']),
h_size=int(trainer_params['hidden_units']),
h_size_vec=int(trainer_params['hidden_units_vec']),
epsilon=float(trainer_params['epsilon']),
beta=float(trainer_params['beta']),
max_step=float(trainer_params['max_steps']),
normalize=trainer_params['normalize'],
use_recurrent=trainer_params['use_recurrent'],
num_layers=int(trainer_params['num_layers']),
m_size=self.m_size,
use_curiosity=bool(trainer_params['use_curiosity']),
curiosity_strength=float(trainer_params['curiosity_strength']),
curiosity_enc_size=float(trainer_params['curiosity_enc_size']),
seed=seed,
forward_model_weight=trainer_params['forward_model_weight'],
use_depth=use_depth,
save_activations=save_activations)
if load:
self._load_graph()
else:
self._initialize_graph()
self.inference_dict = {
'action': self.model.output,
'log_probs': self.model.all_log_probs,
'value': self.model.value,
'entropy': self.model.entropy,
'learning_rate': self.model.learning_rate
}
if self.use_continuous_act:
self.inference_dict['pre_action'] = self.model.output_pre
if self.use_recurrent:
self.inference_dict['memory_out'] = self.model.memory_out
if is_training and self.use_vec_obs and trainer_params['normalize']:
self.inference_dict['update_mean'] = self.model.update_mean
self.inference_dict['update_variance'] = self.model.update_variance
#if self.use_curiosity:
# self.inference_dict['pred_next_state'] = self.model.pred_next_state
self.update_dict = {
'value_loss': self.model.value_loss,
'policy_loss': self.model.policy_loss,
'update_batch': self.model.update_batch
}
self.inference_dict['encoded_state'] = self.model.encoding
if self.save_activations:
self.update_dict['gradients'] = self.model.hidden_grad
self.encodings = []
self.gradients = []
self.values = []
self.actions = []
print(self.update_dict)
if self.use_curiosity:
self.enc_cur_state = []
self.enc_next_state = []
self.pred_state = []
self.pred_act = []
if self.use_curiosity:
self.update_dict['forward_loss'] = self.model.forward_loss
self.update_dict['inverse_loss'] = self.model.inverse_loss
if self.save_activations:
self.inference_dict['enc_cur_state'] = self.model.enc_cur_state
self.inference_dict['pred_state'] = self.model.pred_next_state
self.inference_dict['pred_act'] = self.model.pred_action
self.inference_dict['forward_loss'] = self.model.forward_loss
self.inference_dict['inverse_loss'] = self.model.inverse_loss