def testBatchSizeProvided(self, metric_class, expected_result):
metric = metric_class(batch_size=2)
metric(
nest_utils.stack_nested_arrays([
trajectory.boundary((), (), (), 0., 1.),
trajectory.boundary((), (), (), 0., 1.)
]))
metric(
nest_utils.stack_nested_arrays([
trajectory.first((), (), (), 1., 1.),
trajectory.first((), (), (), 1., 1.)
]))
metric(
nest_utils.stack_nested_arrays([
trajectory.mid((), (), (), 2., 1.),
trajectory.last((), (), (), 3., 0.)
]))
metric(
nest_utils.stack_nested_arrays([
trajectory.last((), (), (), 3., 0.),
trajectory.boundary((), (), (), 0., 1.)
]))
metric(
nest_utils.stack_nested_arrays([
trajectory.boundary((), (), (), 0., 1.),
trajectory.first((), (), (), 1., 1.)
]))
self.assertEqual(metric.result(), expected_result)
def _create_misaligned_trajectories(self):
def _concat_nested_tensors(nest1, nest2):
return tf.nest.map_structure(lambda t1, t2: tf.concat([t1, t2], axis=0),
nest1, nest2)
# Order of args for trajectory methods:
# observation, action, policy_info, reward, discount
ts1 = _concat_nested_tensors(
trajectory.first((), tf.constant([2]), (),
tf.constant([1.], dtype=tf.float32), [1.]),
trajectory.boundary((), tf.constant([1]), (),
tf.constant([0.], dtype=tf.float32), [1.]))
ts2 = _concat_nested_tensors(
trajectory.last((), tf.constant([1]), (),
tf.constant([3.], dtype=tf.float32), [1.]),
trajectory.first((), tf.constant([1]), (),
tf.constant([2.], dtype=tf.float32), [1.]))
ts3 = _concat_nested_tensors(
trajectory.boundary((), tf.constant([2]), (),
tf.constant([0.], dtype=tf.float32), [1.]),
trajectory.last((), tf.constant([1]), (),
tf.constant([4.], dtype=tf.float32), [1.]))
return [ts1, ts2, ts3]
def setUp(self):
super(BatchedPyMetricTest, self).setUp()
# Order of args for trajectory methods:
# observation, action, policy_info, reward, discount
ts0 = nest_utils.stack_nested_tensors([
trajectory.boundary((), (), (), 0., 1.),
trajectory.boundary((), (), (), 0., 1.)
])
ts1 = nest_utils.stack_nested_tensors([
trajectory.first((), (), (), 1., 1.),
trajectory.first((), (), (), 2., 1.)
])
ts2 = nest_utils.stack_nested_tensors([
trajectory.last((), (), (), 3., 1.),
trajectory.last((), (), (), 4., 1.)
])
ts3 = nest_utils.stack_nested_tensors([
trajectory.boundary((), (), (), 0., 1.),
trajectory.boundary((), (), (), 0., 1.)
])
ts4 = nest_utils.stack_nested_tensors([
trajectory.first((), (), (), 5., 1.),
trajectory.first((), (), (), 6., 1.)
])
ts5 = nest_utils.stack_nested_tensors([
trajectory.last((), (), (), 7., 1.),
trajectory.last((), (), (), 8., 1.)
])
self._ts = [ts0, ts1, ts2, ts3, ts4, ts5]
def _create_trajectories(self):
# Order of args for trajectory methods:
# observation, action, policy_info, reward, discount
ts0 = nest_utils.stack_nested_tensors([
trajectory.boundary((), (), (), 0., 1.),
trajectory.boundary((), (), (), 0., 1.)
])
ts1 = nest_utils.stack_nested_tensors([
trajectory.first((), (), (), 1., 1.),
trajectory.first((), (), (), 2., 1.)
])
ts2 = nest_utils.stack_nested_tensors([
trajectory.last((), (), (), 3., 1.),
trajectory.last((), (), (), 4., 1.)
])
ts3 = nest_utils.stack_nested_tensors([
trajectory.boundary((), (), (), 0., 1.),
trajectory.boundary((), (), (), 0., 1.)
])
ts4 = nest_utils.stack_nested_tensors([
trajectory.first((), (), (), 5., 1.),
trajectory.first((), (), (), 6., 1.)
])
ts5 = nest_utils.stack_nested_tensors([
trajectory.last((), (), (), 7., 1.),
trajectory.last((), (), (), 8., 1.)
])
return [ts0, ts1, ts2, ts3, ts4, ts5]
def setUp(self):
super(PyDriverTest, self).setUp()
f0 = np.array(0., dtype=np.float32)
f1 = np.array(1., dtype=np.float32)
# Order of args for trajectory methods:
# (observation, action, policy_info, reward, discount)
self._trajectories = [
trajectory.first(0, 1, 2, f1, f1),
trajectory.last(1, 2, 4, f1, f0),
trajectory.boundary(3, 1, 2, f0, f1),
trajectory.first(0, 1, 2, f1, f1),
trajectory.last(1, 2, 4, f1, f0),
trajectory.boundary(3, 1, 2, f0, f1),
trajectory.first(0, 1, 2, f1, f1),
]
def testZeroEpisodes(self, metric_class, expected_result):
metric = metric_class()
# Order of args for trajectory methods:
# observation, action, policy_info, reward, discount
metric(trajectory.boundary((), (), (), 0., 1.))
metric(trajectory.first((), (), (), 1., 1.))
self.assertEqual(expected_result, metric.result())
def testAverageTwoEpisode(self, metric_class, expected_result):
metric = metric_class()
metric(trajectory.boundary((), (), (), 0., 1.))
metric(trajectory.first((), (), (), 1., 1.))
metric(trajectory.mid((), (), (), 2., 1.))
metric(trajectory.last((), (), (), 3., 0.))
metric(trajectory.boundary((), (), (), 0., 1.))
# TODO(kbanoop): Add optional next_step_type arg to trajectory.first. Or
# implement trajectory.first_last().
metric(
trajectory.Trajectory(ts.StepType.FIRST, (), (), (),
ts.StepType.LAST, -6., 1.))
self.assertEqual(expected_result, metric.result())
def testAverageOneEpisode(self, metric_class, expected_result):
metric = metric_class()
metric(trajectory.boundary((), (), (), 0., 1.))
metric(trajectory.mid((), (), (), 1., 1.))
metric(trajectory.mid((), (), (), 2., 1.))
metric(trajectory.last((), (), (), 3., 0.))
self.assertEqual(expected_result, metric.result())
def _get_padding_step(
self, example_trajectory: trajectory_lib.Trajectory
) -> trajectory_lib.Trajectory:
"""Get the padding step to append to the cache."""
zero_step = trajectory_lib.boundary(
tf.nest.map_structure(tf.zeros_like, example_trajectory.observation),
tf.nest.map_structure(tf.zeros_like, example_trajectory.action),
tf.nest.map_structure(tf.zeros_like, example_trajectory.policy_info),
tf.nest.map_structure(tf.zeros_like, example_trajectory.reward),
tf.nest.map_structure(tf.zeros_like, example_trajectory.discount),
)
return zero_step
def testSaveRestore(self):
metrics = [
py_metrics.AverageReturnMetric(),
py_metrics.AverageEpisodeLengthMetric(),
py_metrics.EnvironmentSteps(),
py_metrics.NumberOfEpisodes()
]
for metric in metrics:
metric(trajectory.boundary((), (), (), 0., 1.))
metric(trajectory.mid((), (), (), 1., 1.))
metric(trajectory.mid((), (), (), 2., 1.))
metric(trajectory.last((), (), (), 3., 0.))
checkpoint = tf.train.Checkpoint(**{m.name: m for m in metrics})
prefix = self.get_temp_dir() + '/ckpt'
save_path = checkpoint.save(prefix)
for metric in metrics:
metric.reset()
self.assertEqual(0, metric.result())
checkpoint.restore(save_path).assert_consumed()
for metric in metrics:
self.assertGreater(metric.result(), 0)
#TRAINING LOOP
for i in tqdm(range(5000), "Training"):
#QUERY TABLE FOR PARTIAL ROWS
regex_filter = '^cartpole_trajectory_{}$'.format(i)
row_filter = row_filters.RowKeyRegexFilter(regex_filter)
filtered_rows = table.read_rows(filter_=row_filter)
for row in filtered_rows:
bytes_traj = row.cells['trajectory']['traj'.encode()][0].value
bytes_info = row.cells['trajectory']['info'.encode()][0].value
traj, info = Trajectory(), Info()
traj.ParseFromString(bytes_traj)
info.ParseFromString(bytes_info)
traj_shape = np.append(np.array(info.num_steps), np.array(info.vector_obs_spec))
observations = np.array(traj.vector_obs).reshape(traj_shape)
traj_obs = np.rollaxis(np.array([observations, np.roll(observations, 1)]), 0 , 2)
traj_actions = np.rollaxis(np.array([traj.actions, np.roll(traj.actions, 1)]), 0 , 2)
traj_rewards = np.rollaxis(np.array([traj.rewards, np.roll(traj.rewards, 1)]), 0 , 2)
traj_discounts = np.ones((info.num_steps,2))
traj_obs = tf.constant(traj_obs, dtype=tf.float32)
traj_actions = tf.constant(traj_actions, dtype=tf.int32)
policy_info = ()
traj_rewards = tf.constant(traj_rewards, dtype=tf.float32)
traj_discounts = tf.constant(traj_discounts, dtype=tf.float32)
traj = trajectory.boundary(traj_obs, traj_actions, policy_info, traj_rewards, traj_discounts)
train_loss = tf_agent.train(traj)
tf_agent._q_network.save_weights("cartpole_model.h5")