def test_param_values(self):
gcb = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False)
new_gcb = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False,
name='GaussianCNNBaseline2')
# Manual change the parameter of GaussianCNNBaseline
with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):
bias_var = tf.compat.v1.get_variable(
'dist_params/mean_network/hidden_0/bias')
bias_var.load(tf.ones_like(bias_var).eval())
old_param_values = gcb.get_param_values()
new_param_values = new_gcb.get_param_values()
assert not np.array_equal(old_param_values, new_param_values)
new_gcb.set_param_values(old_param_values)
new_param_values = new_gcb.get_param_values()
assert np.array_equal(old_param_values, new_param_values)
def test_get_params(self):
gcb = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False)
params_interal = gcb.get_params()
trainable_params = tf.compat.v1.trainable_variables(
scope='GaussianCNNBaseline')
assert np.array_equal(params_interal, trainable_params)
def test_clone(self):
gcb = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False)
cloned_gcb_model = gcb.clone_model(name='cloned_model')
for cloned_param, param in zip(cloned_gcb_model.parameters.values(),
gcb.parameters.values()):
assert np.array_equal(cloned_param, param)
def test_get_params_internal(self, obs_dim):
box_env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
gcb = GaussianCNNBaseline(env_spec=box_env.spec,
regressor_args=dict())
params_interal = gcb.get_params_internal()
trainable_params = tf.compat.v1.trainable_variables(
scope='GaussianCNNBaseline')
assert np.array_equal(params_interal, trainable_params)
def test_flattened_image_input(self):
env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
gcb = GaussianCNNBaseline(env_spec=env.spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ))
env.reset()
es = env.step(1)
obs, rewards = es.observation, es.reward
train_paths = [{'observations': [obs.flatten()], 'returns': [rewards]}]
gcb.fit(train_paths)
paths = {'observations': [obs.flatten()]}
prediction = gcb.predict(paths)
assert np.allclose(0., prediction)
def run_task(snapshot_config, *_):
"""Run task."""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(normalize(gym.make('MemorizeDigits-v0')))
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, 64, 64),
conv_filter_sizes=(5, 3, 2),
conv_strides=(4, 2, 1),
conv_pad='VALID',
hidden_sizes=(256, ))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64, 64),
filter_dims=(5, 3, 2),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ),
use_trust_region=True))
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=1000, batch_size=2048)
def test_trpo_cnn_cubecrash(self):
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = TfEnv(normalize(gym.make('CubeCrash-v0')))
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, 64),
conv_filter_sizes=(8, 4),
conv_strides=(4, 2),
conv_pad='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64),
filter_dims=(8, 4),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True))
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.98,
max_kl_step=0.01,
policy_ent_coeff=0.0,
flatten_input=False)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > -0.9
env.close()
def run_task(snapshot_config, *_):
"""Run task."""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(normalize(gym.make('CubeCrash-v0')))
print('shape= ', env.spec.observation_space.shape)
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, 64),
conv_filter_sizes=(8, 4),
conv_strides=(4, 2),
conv_pad='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64),
filter_dims=(8, 4),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True))
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=4000)
def test_trpo_cnn_cubecrash(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
env = normalize(GymEnv('CubeCrash-v0', max_episode_length=100))
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4,
4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
gae_lambda=0.98,
max_kl_step=0.01,
policy_ent_coeff=0.0)
trainer.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = trainer.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > -1.5
env.close()
def ppo_memorize_digits(ctxt=None,
seed=1,
batch_size=4000,
max_episode_length=100):
"""Train PPO on MemorizeDigits-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
batch_size (int): Number of timesteps to use in each training step.
max_episode_length (int): Max number of timesteps in an episode.
"""
set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = normalize(
GymEnv('MemorizeDigits-v0',
is_image=True,
max_episode_length=max_episode_length))
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=(
(32, (5, 5)),
(64, (3, 3)),
(64, (2, 2)),
),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, )) # yapf: disable
baseline = GaussianCNNBaseline(
env_spec=env.spec,
filters=(
(32, (5, 5)),
(64, (3, 3)),
(64, (2, 2)),
),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ),
use_trust_region=True) # yapf: disable
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
learning_rate=1e-3,
))
trainer.setup(algo, env)
trainer.train(n_epochs=1000, batch_size=batch_size)
def test_fit(self, obs_dim):
box_env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
gcb = GaussianCNNBaseline(env_spec=box_env.spec)
paths = [{
'observations': [np.full(obs_dim, 1)],
'returns': [1]
}, {
'observations': [np.full(obs_dim, 2)],
'returns': [2]
}]
gcb.fit(paths)
obs = {'observations': [np.full(obs_dim, 1), np.full(obs_dim, 2)]}
prediction = gcb.predict(obs)
assert np.array_equal(prediction, [1, 2])
def gaussian_cnn_baseline(ctxt, env_id, seed):
"""Create Gaussian CNN Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = normalize(GymEnv(env_id))
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=params['conv_filters'],
strides=params['conv_strides'],
padding=params['conv_pad'],
hidden_sizes=params['hidden_sizes'])
baseline = GaussianCNNBaseline(
env_spec=env.spec,
filters=params['conv_filters'],
strides=params['conv_strides'],
padding=params['conv_pad'],
hidden_sizes=params['hidden_sizes'],
use_trust_region=params['use_trust_region'])
sampler = RaySampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
learning_rate=1e-3,
),
)
trainer.setup(algo, env)
trainer.train(n_epochs=params['n_epochs'],
batch_size=params['batch_size'])
def categorical_cnn_policy(ctxt, env_id, seed):
"""Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
with LocalTFRunner(ctxt, max_cpus=12) as runner:
env = TfEnv(normalize(gym.make(env_id)))
policy = CategoricalCNNPolicy(
env_spec=env.spec,
conv_filters=hyper_params['conv_filters'],
conv_filter_sizes=hyper_params['conv_filter_sizes'],
conv_strides=hyper_params['conv_strides'],
conv_pad=hyper_params['conv_pad'],
hidden_sizes=hyper_params['hidden_sizes'])
baseline = GaussianCNNBaseline(
env_spec=env.spec,
regressor_args=dict(
num_filters=hyper_params['conv_filters'],
filter_dims=hyper_params['conv_filter_sizes'],
strides=hyper_params['conv_strides'],
padding=hyper_params['conv_pad'],
hidden_sizes=hyper_params['hidden_sizes'],
use_trust_region=hyper_params['use_trust_region']))
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
flatten_input=False,
)
runner.setup(algo, env)
runner.train(n_epochs=hyper_params['n_epochs'],
batch_size=hyper_params['batch_size'])
def test_obs_not_image(self):
env = GarageEnv(DummyDiscretePixelEnv(), is_image=False)
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
with mock.patch(
'garage.tf.baselines.'
'gaussian_cnn_baseline.'
'normalize_pixel_batch',
side_effect=normalize_pixel_batch) as npb:
gcb = GaussianCNNBaseline(env_spec=env.spec)
obs_dim = env.spec.observation_space.shape
paths = [{
'observations': [np.full(obs_dim, 1)],
'returns': [1]
}, {
'observations': [np.full(obs_dim, 2)],
'returns': [2]
}]
gcb.fit(paths)
obs = {
'observations': [np.full(obs_dim, 1),
np.full(obs_dim, 2)]
}
gcb.predict(obs)
assert not npb.called
def test_fit_unnormalized(self):
gcr = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=True,
normalize_inputs=False,
normalize_outputs=False)
train_data, test_data = get_train_test_data()
train_paths, _, _ = train_data
for _ in range(20):
gcr.fit(train_paths)
test_paths, expected = test_data
prediction = gcr.predict(test_paths)
average_error = 0.0
for i, exp in enumerate(expected):
average_error += np.abs(exp - prediction[i])
average_error /= len(expected)
assert average_error <= 0.1
x_mean = self.sess.run(gcr._networks['default'].x_mean)
x_mean_expected = np.zeros_like(x_mean)
x_std = self.sess.run(gcr._networks['default'].x_std)
x_std_expected = np.ones_like(x_std)
assert np.array_equal(x_mean, x_mean_expected)
assert np.array_equal(x_std, x_std_expected)
y_mean = self.sess.run(gcr._networks['default'].y_mean)
y_mean_expected = np.zeros_like(y_mean)
y_std = self.sess.run(gcr._networks['default'].y_std)
y_std_expected = np.ones_like(y_std)
assert np.allclose(y_mean, y_mean_expected)
assert np.allclose(y_std, y_std_expected)
def test_fit_without_trusted_region(self):
gcr = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False)
train_data, test_data = get_train_test_data()
train_paths, _, _ = train_data
for _ in range(20):
gcr.fit(train_paths)
test_paths, expected = test_data
prediction = gcr.predict(test_paths)
average_error = 0.0
for i, exp in enumerate(expected):
average_error += np.abs(exp - prediction[i])
average_error /= len(expected)
assert average_error <= 0.1
def test_fit_normalized(self):
gcr = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=True)
train_data, test_data = get_train_test_data()
train_paths, observations, returns = train_data
for _ in range(20):
gcr.fit(train_paths)
test_paths, expected = test_data
prediction = gcr.predict(test_paths)
average_error = 0.0
for i, exp in enumerate(expected):
average_error += np.abs(exp - prediction[i])
average_error /= len(expected)
assert average_error <= 0.1
x_mean = self.sess.run(gcr._networks['default'].x_mean)
x_mean_expected = np.mean(observations, axis=0, keepdims=True)
x_std = self.sess.run(gcr._networks['default'].x_std)
x_std_expected = np.std(observations, axis=0, keepdims=True)
assert np.allclose(x_mean, x_mean_expected)
assert np.allclose(x_std, x_std_expected)
y_mean = self.sess.run(gcr._networks['default'].y_mean)
y_mean_expected = np.mean(returns, axis=0, keepdims=True)
y_std = self.sess.run(gcr._networks['default'].y_std)
y_std_expected = np.std(returns, axis=0, keepdims=True)
assert np.allclose(y_mean, y_mean_expected)
assert np.allclose(y_std, y_std_expected)
def test_optimizer_args(self, mock_lbfgs):
lbfgs_args = dict(max_opt_itr=25)
gcr = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
optimizer=LbfgsOptimizer,
optimizer_args=lbfgs_args,
use_trust_region=True)
assert mock_lbfgs.return_value is gcr._optimizer
mock_lbfgs.assert_called_with(max_opt_itr=25)
def test_is_pickleable(self):
box_env = GarageEnv(DummyBoxEnv(obs_dim=(1, 1)))
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
gcb = GaussianCNNBaseline(env_spec=box_env.spec)
obs = {'observations': [np.full((1, 1), 1), np.full((1, 1), 1)]}
with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):
return_var = tf.compat.v1.get_variable(
'SimpleGaussianCNNModel/return_var')
return_var.load(1.0)
prediction = gcb.predict(obs)
h = pickle.dumps(gcb)
with tf.compat.v1.Session(graph=tf.Graph()):
gcb_pickled = pickle.loads(h)
prediction2 = gcb_pickled.predict(obs)
assert np.array_equal(prediction, prediction2)
def test_is_pickleable(self):
gcr = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False)
with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):
bias = tf.compat.v1.get_variable(
'dist_params/mean_network/hidden_0/bias')
bias.load(tf.ones_like(bias).eval())
_, test_data = get_train_test_data()
test_paths, _ = test_data
result1 = gcr.predict(test_paths)
h = pickle.dumps(gcr)
with tf.compat.v1.Session(graph=tf.Graph()):
gcr_pickled = pickle.loads(h)
result2 = gcr_pickled.predict(test_paths)
assert np.array_equal(result1, result2)
def ppo_memorize_digits(ctxt=None, seed=1, batch_size=4000):
"""Train PPO on MemorizeDigits-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
batch_size (int): Number of timesteps to use in each training step.
"""
set_seed(seed)
with LocalTFRunner(ctxt) as runner:
env = TfEnv(normalize(gym.make('MemorizeDigits-v0')), is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
num_filters=(32, 64, 64),
filter_dims=(5, 3, 2),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64, 64),
filter_dims=(5, 3, 2),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ),
use_trust_region=True))
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=1000, batch_size=batch_size)
def test_param_values(self, obs_dim):
box_env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
gcb = GaussianCNNBaseline(env_spec=box_env.spec)
new_gcb = GaussianCNNBaseline(env_spec=box_env.spec,
name='GaussianCNNBaseline2')
# Manual change the parameter of GaussianCNNBaseline
with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):
return_var = tf.compat.v1.get_variable(
'SimpleGaussianCNNModel/return_var')
return_var.load(1.0)
old_param_values = gcb.get_param_values()
new_param_values = new_gcb.get_param_values()
assert not np.array_equal(old_param_values, new_param_values)
new_gcb.set_param_values(old_param_values)
new_param_values = new_gcb.get_param_values()
assert np.array_equal(old_param_values, new_param_values)
def trpo_cubecrash(ctxt=None, seed=1, max_episode_length=5, batch_size=4000):
"""Train TRPO with CubeCrash-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
max_episode_length (int): Maximum length of a single episode.
batch_size (int): Number of timesteps to use in each training step.
"""
set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = normalize(
GymEnv('CubeCrash-v0', max_episode_length=max_episode_length))
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True)
sampler = RaySampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0)
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=batch_size)
def run_task(snapshot_config, variant_data, *_):
"""Run task.
Args:
snapshot_config (garage.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
variant_data (dict): Custom arguments for the task.
*_ (object): Ignored by this function.
"""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(normalize(gym.make('MemorizeDigits-v0')), is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, 64, 64),
conv_filter_sizes=(5, 3, 2),
conv_strides=(4, 2, 1),
conv_pad='VALID',
hidden_sizes=(256, ))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64, 64),
filter_dims=(5, 3, 2),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ),
use_trust_region=True))
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=1000, batch_size=variant_data['batch_size'])
def trpo_cubecrash(ctxt=None, seed=1, batch_size=4000):
"""Train TRPO with CubeCrash-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
batch_size (int): Number of timesteps to use in each training step.
"""
set_seed(seed)
with LocalTFRunner(ctxt) as runner:
env = TfEnv(normalize(gym.make('CubeCrash-v0')))
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, 64),
conv_filter_sizes=(8, 4),
conv_strides=(4, 2),
conv_pad='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64),
filter_dims=(8, 4),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True))
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=batch_size)
def trpo_cubecrash(ctxt=None, seed=1, batch_size=4000):
"""Train TRPO with CubeCrash-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
batch_size (int): Number of timesteps to use in each training step.
"""
set_seed(seed)
with LocalTFRunner(ctxt) as runner:
env = GarageEnv(normalize(gym.make('CubeCrash-v0')))
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=batch_size)
def test_obs_is_image(self):
env = GarageEnv(DummyDiscretePixelEnv(), is_image=True)
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
with mock.patch(
'garage.tf.baselines.'
'gaussian_cnn_baseline.'
'normalize_pixel_batch',
side_effect=normalize_pixel_batch) as npb:
gcb = GaussianCNNBaseline(env_spec=env.spec)
obs_dim = env.spec.observation_space.shape
paths = [{
'observations': [np.full(obs_dim, 1)],
'returns': [1]
}, {
'observations': [np.full(obs_dim, 2)],
'returns': [2]
}]
gcb.fit(paths)
observations = np.concatenate(
[p['observations'] for p in paths])
assert npb.call_count == 1, (
"Expected '%s' to have been called once. Called %s times."
% (npb._mock_name or 'mock', npb.call_count))
assert (npb.call_args_list[0][0][0] == observations).all()
obs = {
'observations': [np.full(obs_dim, 1),
np.full(obs_dim, 2)]
}
observations = obs['observations']
gcb.predict(obs)
assert npb.call_args_list[1][0][0] == observations
def run_garage(env, seed, log_dir):
'''
Create garage model and training.
Replace the ppo with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:
'''
deterministic.set_seed(seed)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=12,
inter_op_parallelism_threads=12)
sess = tf.Session(config=config)
with LocalTFRunner(snapshot_config, sess=sess, max_cpus=12) as runner:
env = TfEnv(normalize(env))
policy = CategoricalCNNPolicy(
env_spec=env.spec,
conv_filters=params['conv_filters'],
conv_filter_sizes=params['conv_filter_sizes'],
conv_strides=params['conv_strides'],
conv_pad=params['conv_pad'],
hidden_sizes=params['hidden_sizes'])
baseline = GaussianCNNBaseline(
env_spec=env.spec,
regressor_args=dict(num_filters=params['conv_filters'],
filter_dims=params['conv_filter_sizes'],
strides=params['conv_strides'],
padding=params['conv_pad'],
hidden_sizes=params['hidden_sizes'],
use_trust_region=params['use_trust_region']))
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
flatten_input=False,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=params['n_epochs'],
batch_size=params['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def test_invalid_obs_shape(self, obs_dim):
box_env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim))
with pytest.raises(ValueError):
GaussianCNNBaseline(env_spec=box_env.spec)
