def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
"""
Responsible for collecting experiences and training PPO model.
:param trainer_parameters: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
:param load: Whether the model should be loaded.
:param seed: The seed the model will be initialized with
:param run_id: The The identifier of the current run
"""
super(BCTrainer, self).__init__(brain, trainer_parameters, training, run_id)
self.policy = BCPolicy(seed, brain, trainer_parameters, load)
self.n_sequences = 1
self.cumulative_rewards = {}
self.episode_steps = {}
self.stats = {'Losses/Cloning Loss': [], 'Environment/Episode Length': [],
'Environment/Cumulative Reward': []}
self.summary_path = trainer_parameters['summary_path']
self.batches_per_epoch = trainer_parameters['batches_per_epoch']
if not os.path.exists(self.summary_path):
os.makedirs(self.summary_path)
self.demonstration_buffer = Buffer()
self.evaluation_buffer = Buffer()
self.summary_writer = tf.summary.FileWriter(self.summary_path)
def __init__(self, brain, trainer_parameters, training, load, seed,
run_id):
"""
Responsible for collecting experiences and training PPO model.
:param trainer_parameters: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
:param load: Whether the model should be loaded.
:param seed: The seed the model will be initialized with
:param run_id: The identifier of the current run
"""
super(BCTrainer, self).__init__(brain, trainer_parameters, training,
run_id)
self.policy = BCPolicy(seed, brain, trainer_parameters, load)
self.n_sequences = 1
self.cumulative_rewards = {}
self.episode_steps = {}
self.stats = {
"Losses/Cloning Loss": [],
"Environment/Episode Length": [],
"Environment/Cumulative Reward": [],
}
self.batches_per_epoch = trainer_parameters["batches_per_epoch"]
self.demonstration_buffer = Buffer()
self.evaluation_buffer = Buffer()
def __init__(self, sess, brain, trainer_parameters, training, seed, run_id):
"""
Responsible for collecting experiences and training PPO model.
:param sess: Tensorflow session.
:param trainer_parameters: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
"""
super(BehavioralCloningTrainer, self).__init__(sess, brain, trainer_parameters, training, run_id)
self.param_keys = ['brain_to_imitate', 'batch_size', 'time_horizon',
'graph_scope', 'summary_freq', 'max_steps',
'batches_per_epoch', 'use_recurrent',
'hidden_units','learning_rate', 'num_layers',
'sequence_length', 'memory_size']
for k in self.param_keys:
if k not in trainer_parameters:
raise UnityTrainerException("The hyperparameter {0} could not be found for the Imitation trainer of "
"brain {1}.".format(k, brain.brain_name))
self.policy = BCPolicy(seed, brain, trainer_parameters, sess)
self.brain_name = brain.brain_name
self.brain_to_imitate = trainer_parameters['brain_to_imitate']
self.batches_per_epoch = trainer_parameters['batches_per_epoch']
self.n_sequences = max(int(trainer_parameters['batch_size'] / self.policy.sequence_length), 1)
self.cumulative_rewards = {}
self.episode_steps = {}
self.stats = {'losses': [], 'episode_length': [], 'cumulative_reward': []}
self.training_buffer = Buffer()
self.summary_path = trainer_parameters['summary_path']
if not os.path.exists(self.summary_path):
os.makedirs(self.summary_path)
self.summary_writer = tf.summary.FileWriter(self.summary_path)
def test_bc_policy_evaluate(mock_communicator, mock_launcher, dummy_config):
tf.reset_default_graph()
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain_infos = env.reset()
brain_info = brain_infos[env.brain_names[0]]
trainer_parameters = dummy_config
model_path = env.brain_names[0]
trainer_parameters['model_path'] = model_path
trainer_parameters['keep_checkpoints'] = 3
policy = BCPolicy(0, env.brains[env.brain_names[0]], trainer_parameters, False)
run_out = policy.evaluate(brain_info)
assert run_out['action'].shape == (3, 2)
env.close()
def test_bc_policy_evaluate(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain_infos = env.reset()
brain_info = brain_infos[env.brain_names[0]]
trainer_parameters = dummy_config()
graph_scope = env.brain_names[0]
trainer_parameters['graph_scope'] = graph_scope
policy = BCPolicy(0, env.brains[env.brain_names[0]],
trainer_parameters, sess)
init = tf.global_variables_initializer()
sess.run(init)
run_out = policy.evaluate(brain_info)
assert run_out['action'].shape == (3, 2)
env.close()
def test_bc_policy_evaluate(mock_communicator, mock_launcher, dummy_config):
tf.reset_default_graph()
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0)
env = UnityEnvironment(" ")
env.reset()
brain_name = env.get_agent_groups()[0]
brain_info = step_result_to_brain_info(
env.get_step_result(brain_name), env.get_agent_group_spec(brain_name))
brain_params = group_spec_to_brain_parameters(
brain_name, env.get_agent_group_spec(brain_name))
trainer_parameters = dummy_config
model_path = brain_name
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
policy = BCPolicy(0, brain_params, trainer_parameters, False)
run_out = policy.evaluate(brain_info)
assert run_out["action"].shape == (3, 2)
env.close()