def _restore(self, checkpoint_dir):
assert isinstance(checkpoint_dir, str), checkpoint_dir
checkpoint_dir = checkpoint_dir.rstrip('/')
with self._session.as_default():
pickle_path = self._pickle_path(checkpoint_dir)
with open(pickle_path, 'rb') as f:
picklable = pickle.load(f)
training_environment = self.training_environment = picklable[
'training_environment']
evaluation_environment = self.evaluation_environment = picklable[
'evaluation_environment']
replay_pool = self.replay_pool = (
get_replay_pool_from_variant(self._variant, training_environment))
if self._variant['run_params'].get('checkpoint_replay_pool', False):
self._restore_replay_pool(checkpoint_dir)
sampler = self.sampler = picklable['sampler']
Qs = self.Qs = get_Q_function_from_variant(
self._variant, training_environment)
self._restore_value_functions(checkpoint_dir)
policy = self.policy = (
get_policy_from_variant(self._variant, training_environment))
self.policy.set_weights(picklable['policy_weights'])
initial_exploration_policy = self.initial_exploration_policy = (
get_policy_from_params(
self._variant['exploration_policy_params'],
training_environment))
self.algorithm = get_algorithm_from_variant(
variant=self._variant,
training_environment=training_environment,
evaluation_environment=evaluation_environment,
policy=policy,
initial_exploration_policy=initial_exploration_policy,
Qs=Qs,
pool=replay_pool,
sampler=sampler,
session=self._session)
self.algorithm.__setstate__(picklable['algorithm'].__getstate__())
tf_checkpoint = self._get_tf_checkpoint()
status = tf_checkpoint.restore(tf.train.latest_checkpoint(
os.path.split(self._tf_checkpoint_prefix(checkpoint_dir))[0]))
status.assert_consumed().run_restore_ops(self._session)
initialize_tf_variables(self._session, only_uninitialized=True)
# TODO(hartikainen): target Qs should either be checkpointed or pickled.
for Q, Q_target in zip(self.algorithm._Qs, self.algorithm._Q_targets):
Q_target.set_weights(Q.get_weights())
self._built = True
def run_experiment(variant, reporter):
training_environment = (
get_environment('gym', 'MultiGoal', 'Default-v0', {
'actuation_cost_coeff': 30,
'distance_cost_coeff': 1,
'goal_reward': 10,
'init_sigma': 0.1,
}))
evaluation_environment = training_environment.copy()
pool = SimpleReplayPool(
environment=training_environment,
max_size=1e6)
sampler = SimpleSampler(max_path_length=30)
Qs = get_Q_function_from_variant(variant, training_environment)
policy = get_policy_from_variant(variant, training_environment)
plotter = QFPolicyPlotter(
Q=Qs[0],
policy=policy,
obs_lst=np.array(((-2.5, 0.0),
(0.0, 0.0),
(2.5, 2.5),
(-2.5, -2.5))),
default_action=(np.nan, np.nan),
n_samples=100)
algorithm = get_algorithm_from_variant(
variant=variant,
training_environment=training_environment,
evaluation_environment=evaluation_environment,
policy=policy,
Qs=Qs,
pool=pool,
sampler=sampler,
min_pool_size=100,
batch_size=46,
plotter=plotter,
)
initialize_tf_variables(algorithm._session, only_uninitialized=True)
for train_result in algorithm.train():
reporter(**train_result)
def _build(self):
variant = copy.deepcopy(self._variant)
environment_params = variant['environment_params']
training_environment = self.training_environment = (
get_environment_from_params(environment_params['training']))
evaluation_environment = self.evaluation_environment = (
get_environment_from_params(environment_params['evaluation'])
if 'evaluation' in environment_params
else training_environment)
replay_pool = self.replay_pool = (
get_replay_pool_from_variant(variant, training_environment))
sampler = self.sampler = get_sampler_from_variant(variant)
Qs = self.Qs = get_Q_function_from_variant(
variant, training_environment)
policy = self.policy = get_policy_from_variant(
variant, training_environment)
initial_exploration_policy = self.initial_exploration_policy = (
get_policy_from_params(
variant['exploration_policy_params'], training_environment))
self.algorithm = get_algorithm_from_variant(
variant=self._variant,
training_environment=training_environment,
evaluation_environment=evaluation_environment,
policy=policy,
initial_exploration_policy=initial_exploration_policy,
Qs=Qs,
pool=replay_pool,
sampler=sampler,
session=self._session)
initialize_tf_variables(self._session, only_uninitialized=True)
self._built = True