def test_normalizer_after_load(tmp_path):
behavior_spec = mb.setup_test_behavior_specs(
use_discrete=True, use_visual=False, vector_action_space=[2], vector_obs_space=1
)
time_horizon = 6
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_spec=behavior_spec.action_spec,
)
# Change half of the obs to 0
for i in range(3):
trajectory.steps[i].obs[0] = np.zeros(1, dtype=np.float32)
trainer_params = TrainerSettings(network_settings=NetworkSettings(normalize=True))
policy = TFPolicy(0, behavior_spec, trainer_params)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run(
[policy.normalization_steps, policy.running_mean, policy.running_variance]
)
assert steps == 6
assert mean[0] == 0.5
assert variance[0] / steps == pytest.approx(0.25, abs=0.01)
# Save ckpt and load into another policy
path1 = os.path.join(tmp_path, "runid1")
model_saver = TFModelSaver(trainer_params, path1)
model_saver.register(policy)
mock_brain_name = "MockBrain"
model_saver.save_checkpoint(mock_brain_name, 6)
assert len(os.listdir(tmp_path)) > 0
policy1 = TFPolicy(0, behavior_spec, trainer_params)
model_saver = TFModelSaver(trainer_params, path1, load=True)
model_saver.register(policy1)
model_saver.initialize_or_load(policy1)
# Make another update to new policy, this time with all 1's
time_horizon = 10
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_spec=behavior_spec.action_spec,
)
trajectory_buffer = trajectory.to_agentbuffer()
policy1.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy1.sess.run(
[policy1.normalization_steps, policy1.running_mean, policy1.running_variance]
)
assert steps == 16
assert mean[0] == 0.8125
assert variance[0] / steps == pytest.approx(0.152, abs=0.01)
def test_normalization():
behavior_spec = mb.setup_test_behavior_specs(use_discrete=True,
use_visual=False,
vector_action_space=[2],
vector_obs_space=1)
time_horizon = 6
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1, )],
action_space=[2],
)
# Change half of the obs to 0
for i in range(3):
trajectory.steps[i].obs[0] = np.zeros(1, dtype=np.float32)
policy = TFPolicy(
0,
behavior_spec,
TrainerSettings(network_settings=NetworkSettings(normalize=True)),
"testdir",
False,
)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run([
policy.normalization_steps, policy.running_mean,
policy.running_variance
])
assert steps == 6
assert mean[0] == 0.5
# Note: variance is divided by number of steps, and initialized to 1 to avoid
# divide by 0. The right answer is 0.25
assert (variance[0] - 1) / steps == 0.25
# Make another update, this time with all 1's
time_horizon = 10
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1, )],
action_space=[2],
)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run([
policy.normalization_steps, policy.running_mean,
policy.running_variance
])
assert steps == 16
assert mean[0] == 0.8125
assert (variance[0] - 1) / steps == pytest.approx(0.152, abs=0.01)
def test_large_normalization():
behavior_spec = mb.setup_test_behavior_specs(
use_discrete=True, use_visual=False, vector_action_space=[2], vector_obs_space=1
)
# Taken from Walker seed 3713 which causes NaN without proper initialization
large_obs1 = [
1800.00036621,
1799.96972656,
1800.01245117,
1800.07214355,
1800.02758789,
1799.98303223,
1799.88647461,
1799.89575195,
1800.03479004,
1800.14025879,
1800.17675781,
1800.20581055,
1800.33740234,
1800.36450195,
1800.43457031,
1800.45544434,
1800.44604492,
1800.56713867,
1800.73901367,
]
large_obs2 = [
1799.99975586,
1799.96679688,
1799.92980957,
1799.89550781,
1799.93774414,
1799.95300293,
1799.94067383,
1799.92993164,
1799.84057617,
1799.69873047,
1799.70605469,
1799.82849121,
1799.85095215,
1799.76977539,
1799.78283691,
1799.76708984,
1799.67163086,
1799.59191895,
1799.5135498,
1799.45556641,
1799.3717041,
]
policy = TFPolicy(
0,
behavior_spec,
TrainerSettings(network_settings=NetworkSettings(normalize=True)),
"testdir",
False,
)
time_horizon = len(large_obs1)
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_space=[2],
)
for i in range(time_horizon):
trajectory.steps[i].obs[0] = np.array([large_obs1[i]], dtype=np.float32)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
# Check that the running mean and variance is correct
steps, mean, variance = policy.sess.run(
[policy.normalization_steps, policy.running_mean, policy.running_variance]
)
assert mean[0] == pytest.approx(np.mean(large_obs1, dtype=np.float32), abs=0.01)
assert variance[0] / steps == pytest.approx(
np.var(large_obs1, dtype=np.float32), abs=0.01
)
time_horizon = len(large_obs2)
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
observation_shapes=[(1,)],
action_space=[2],
)
for i in range(time_horizon):
trajectory.steps[i].obs[0] = np.array([large_obs2[i]], dtype=np.float32)
trajectory_buffer = trajectory.to_agentbuffer()
policy.update_normalization(trajectory_buffer["vector_obs"])
steps, mean, variance = policy.sess.run(
[policy.normalization_steps, policy.running_mean, policy.running_variance]
)
assert mean[0] == pytest.approx(
np.mean(large_obs1 + large_obs2, dtype=np.float32), abs=0.01
)
assert variance[0] / steps == pytest.approx(
np.var(large_obs1 + large_obs2, dtype=np.float32), abs=0.01
)
