def test_get_params_internal(self, obs_dim):
box_env = TfEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'continuous_mlp_baseline_with_model.'
'ContinuousMLPRegressorWithModel'),
new=SimpleMLPRegressor):
cmb = ContinuousMLPBaselineWithModel(env_spec=box_env.spec)
params_interal = cmb.get_params_internal()
trainable_params = tf.trainable_variables(
scope='ContinuousMLPBaselineWithModel')
assert np.array_equal(params_interal, trainable_params)
def test_ppo_pendulum_continuous_baseline(self):
"""Test PPO with Pendulum environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = ContinuousMLPBaselineWithModel(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
lr_clip_range=0.01,
optimizer_args=dict(batch_size=32, max_epochs=10))
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 30
env.close()
def test_ppo_pendulum_recurrent_continuous_baseline(self):
"""Test PPO with Pendulum environment and recurrent policy."""
with LocalTFRunner(snapshot_config) as runner:
env = TfEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianLSTMPolicy(env_spec=env.spec, )
baseline = ContinuousMLPBaselineWithModel(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
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,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 100
env.close()
def test_fit(self, obs_dim):
box_env = TfEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'continuous_mlp_baseline_with_model.'
'ContinuousMLPRegressorWithModel'),
new=SimpleMLPRegressor):
cmb = ContinuousMLPBaselineWithModel(env_spec=box_env.spec)
paths = [{
'observations': [np.full(obs_dim, 1)],
'returns': [1]
}, {
'observations': [np.full(obs_dim, 2)],
'returns': [2]
}]
cmb.fit(paths)
obs = {'observations': [np.full(obs_dim, 1), np.full(obs_dim, 2)]}
prediction = cmb.predict(obs)
assert np.array_equal(prediction, [1, 2])
def test_is_pickleable(self):
box_env = TfEnv(DummyBoxEnv(obs_dim=(1, )))
with mock.patch(('garage.tf.baselines.'
'continuous_mlp_baseline_with_model.'
'ContinuousMLPRegressorWithModel'),
new=SimpleMLPRegressor):
cmb = ContinuousMLPBaselineWithModel(env_spec=box_env.spec)
obs = {'observations': [np.full(1, 1), np.full(1, 1)]}
with tf.variable_scope('ContinuousMLPBaselineWithModel', reuse=True):
return_var = tf.get_variable('SimpleMLPModel/return_var')
return_var.load(1.0)
prediction = cmb.predict(obs)
h = pickle.dumps(cmb)
with tf.Session(graph=tf.Graph()):
cmb_pickled = pickle.loads(h)
prediction2 = cmb_pickled.predict(obs)
assert np.array_equal(prediction, prediction2)
def test_param_values(self, obs_dim):
box_env = TfEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'continuous_mlp_baseline_with_model.'
'ContinuousMLPRegressorWithModel'),
new=SimpleMLPRegressor):
cmb = ContinuousMLPBaselineWithModel(env_spec=box_env.spec)
new_cmb = ContinuousMLPBaselineWithModel(
env_spec=box_env.spec, name='ContinuousMLPBaselineWithModel2')
# Manual change the parameter of ContinuousMLPBaselineWithModel
with tf.variable_scope('ContinuousMLPBaselineWithModel2', reuse=True):
return_var = tf.get_variable('SimpleMLPModel/return_var')
return_var.load(1.0)
old_param_values = cmb.get_param_values()
new_param_values = new_cmb.get_param_values()
assert not np.array_equal(old_param_values, new_param_values)
new_cmb.set_param_values(old_param_values)
new_param_values = new_cmb.get_param_values()
assert np.array_equal(old_param_values, new_param_values)
