def setup_method(self):
with mock.patch('tensorflow.random.normal') as mock_rand:
mock_rand.return_value = 0.5
super().setup_method()
self.box_env = TfEnv(DummyBoxEnv())
self.policy1 = ContinuousMLPPolicy(
env_spec=self.box_env, hidden_sizes=(32, 32), name='P1')
self.policy2 = ContinuousMLPPolicy(
env_spec=self.box_env, hidden_sizes=(64, 64), name='P2')
self.policy3 = ContinuousMLPPolicyWithModel(
env_spec=self.box_env, hidden_sizes=(32, 32), name='P3')
self.policy4 = ContinuousMLPPolicyWithModel(
env_spec=self.box_env, hidden_sizes=(64, 64), name='P4')
self.sess.run(tf.compat.v1.global_variables_initializer())
for a, b in zip(self.policy3.get_params(),
self.policy1.get_params()):
self.sess.run(a.assign(b))
for a, b in zip(self.policy4.get_params(),
self.policy2.get_params()):
self.sess.run(a.assign(b))
self.obs = self.box_env.reset()
self.action_bound = self.box_env.action_space.high
assert self.policy1.vectorized == self.policy2.vectorized
assert self.policy3.vectorized == self.policy4.vectorized
def test_is_pickleable(self, obs_dim, action_dim):
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'continuous_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = ContinuousMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
with tf.compat.v1.variable_scope('ContinuousMLPPolicy/MLPModel',
reuse=True):
return_var = tf.compat.v1.get_variable('return_var')
# assign it to all one
return_var.load(tf.ones_like(return_var).eval())
output1 = self.sess.run(
policy.model.outputs,
feed_dict={policy.model.input: [obs.flatten()]})
p = pickle.dumps(policy)
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
policy_pickled = pickle.loads(p)
output2 = sess.run(
policy_pickled.model.outputs,
feed_dict={policy_pickled.model.input: [obs.flatten()]})
assert np.array_equal(output1, output2)
def run_garage(env, seed, log_dir):
'''
Create garage model and training.
Replace the ddpg 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)
with LocalRunner() as runner:
env = TfEnv(normalize(env))
# Set up params for ddpg
action_noise = OUStrategy(env.spec, sigma=params['sigma'])
policy = ContinuousMLPPolicyWithModel(
env_spec=env.spec,
hidden_sizes=params['policy_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=params['qf_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(
env_spec=env.spec,
size_in_transitions=params['replay_buffer_size'],
time_horizon=params['n_rollout_steps'])
ddpg = DDPG(env_spec=env.spec,
policy=policy,
qf=qf,
replay_buffer=replay_buffer,
policy_lr=params['policy_lr'],
qf_lr=params['qf_lr'],
target_update_tau=params['tau'],
n_train_steps=params['n_train_steps'],
discount=params['discount'],
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
policy_optimizer=tf.train.AdamOptimizer,
qf_optimizer=tf.train.AdamOptimizer)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
tensorboard_log_dir = osp.join(log_dir)
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(tensorboard_log_dir))
runner.setup(ddpg, env)
runner.train(n_epochs=params['n_epochs'],
n_epoch_cycles=params['n_epoch_cycles'],
batch_size=params['n_rollout_steps'])
dowel_logger.remove_all()
return tabular_log_file
def test_no_reset(self):
with LocalRunner(sess=self.sess) as runner:
# This tests if off-policy sampler respect batch_size
# when no_reset is set to True
env = TfEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
action_noise = OUStrategy(env.spec, sigma=0.2)
policy = ContinuousMLPPolicyWithModel(
env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(
env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(
env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=100)
algo = DDPG(
env_spec=env.spec,
policy=policy,
policy_lr=1e-4,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
target_update_tau=1e-2,
n_train_steps=50,
discount=0.9,
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
)
sampler = OffPolicyVectorizedSampler(algo, env, 1, no_reset=True)
sampler.start_worker()
runner.initialize_tf_vars()
paths1 = sampler.obtain_samples(0, 5)
paths2 = sampler.obtain_samples(0, 5)
len1 = sum([len(path['rewards']) for path in paths1])
len2 = sum([len(path['rewards']) for path in paths2])
assert len1 == 5 and len2 == 5, 'Sampler should respect batch_size'
# yapf: disable
assert (len(paths1[0]['rewards']) + len(paths2[0]['rewards'])
== paths2[0]['running_length']), (
'Running length should be the length of full path')
# yapf: enable
assert np.isclose(
paths1[0]['rewards'].sum() + paths2[0]['rewards'].sum(),
paths2[0]['undiscounted_return']
), 'Undiscounted_return should be the sum of rewards of full path'
def test_get_action(self, obs_dim, action_dim):
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'continuous_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = ContinuousMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
action, _ = policy.get_action(obs)
expected_action = np.full(action_dim, 0.5)
assert env.action_space.contains(action)
assert np.array_equal(action, expected_action)
actions, _ = policy.get_actions([obs, obs, obs])
for action in actions:
assert env.action_space.contains(action)
assert np.array_equal(action, expected_action)
def run_task(snapshot_config, *_):
"""Run task."""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(gym.make('FetchReach-v1'))
action_noise = OUStrategy(env.spec, sigma=0.2)
policy = ContinuousMLPPolicyWithModel(
env_spec=env.spec,
name='Policy',
hidden_sizes=[256, 256, 256],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh,
input_include_goal=True,
)
qf = ContinuousMLPQFunction(
env_spec=env.spec,
name='QFunction',
hidden_sizes=[256, 256, 256],
hidden_nonlinearity=tf.nn.relu,
input_include_goal=True,
)
replay_buffer = HerReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=100,
replay_k=0.4,
reward_fun=env.compute_reward)
ddpg = DDPG(
env_spec=env.spec,
policy=policy,
policy_lr=1e-3,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
target_update_tau=0.05,
n_epoch_cycles=20,
max_path_length=100,
n_train_steps=40,
discount=0.9,
exploration_strategy=action_noise,
policy_optimizer=tf.train.AdamOptimizer,
qf_optimizer=tf.train.AdamOptimizer,
buffer_batch_size=256,
input_include_goal=True,
)
runner.setup(algo=ddpg, env=env)
runner.train(n_epochs=50, batch_size=100, n_epoch_cycles=20)
def test_get_action_sym(self, obs_dim, action_dim):
env = TfEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
with mock.patch(('garage.tf.policies.'
'continuous_mlp_policy_with_model.MLPModel'),
new=SimpleMLPModel):
policy = ContinuousMLPPolicyWithModel(env_spec=env.spec)
env.reset()
obs, _, _, _ = env.step(1)
obs_dim = env.spec.observation_space.flat_dim
state_input = tf.placeholder(tf.float32, shape=(None, obs_dim))
action_sym = policy.get_action_sym(state_input, name='action_sym')
expected_action = np.full(action_dim, 0.5)
action = self.sess.run(action_sym,
feed_dict={state_input: [obs.flatten()]})
action = policy.action_space.unflatten(action)
assert np.array_equal(action, expected_action)
assert env.action_space.contains(action)
def test_ddpg_pendulum(self):
"""
Test DDPG with Pendulum environment.
This environment has a [-3, 3] action_space bound.
"""
with LocalRunner(sess=self.sess) as runner:
env = TfEnv(normalize(gym.make('InvertedPendulum-v2')))
action_noise = OUStrategy(env.spec, sigma=0.2)
policy = ContinuousMLPPolicyWithModel(
env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=100)
algo = DDPG(
env_spec=env.spec,
policy=policy,
policy_lr=1e-4,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
target_update_tau=1e-2,
n_train_steps=50,
discount=0.9,
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10,
n_epoch_cycles=20,
batch_size=100)
assert last_avg_ret > 10
env.close()
def run_task(snapshot_config, *_):
"""Run task."""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(gym.make('InvertedDoublePendulum-v2'))
action_noise = OUStrategy(env.spec, sigma=0.2)
policy = ContinuousMLPPolicyWithModel(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=100)
ddpg = DDPG(env_spec=env.spec,
policy=policy,
policy_lr=1e-4,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
target_update_tau=1e-2,
n_train_steps=50,
discount=0.9,
min_buffer_size=int(1e4),
exploration_strategy=action_noise,
policy_optimizer=tf.train.AdamOptimizer,
qf_optimizer=tf.train.AdamOptimizer)
runner.setup(algo=ddpg, env=env)
runner.train(n_epochs=500, n_epoch_cycles=20, batch_size=100)
class TestContinuousMLPPolicyWithModelTransit(TfGraphTestCase):
def setup_method(self):
with mock.patch('tensorflow.random.normal') as mock_rand:
mock_rand.return_value = 0.5
super().setup_method()
self.box_env = TfEnv(DummyBoxEnv())
self.policy1 = ContinuousMLPPolicy(
env_spec=self.box_env, hidden_sizes=(32, 32), name='P1')
self.policy2 = ContinuousMLPPolicy(
env_spec=self.box_env, hidden_sizes=(64, 64), name='P2')
self.policy3 = ContinuousMLPPolicyWithModel(
env_spec=self.box_env, hidden_sizes=(32, 32), name='P3')
self.policy4 = ContinuousMLPPolicyWithModel(
env_spec=self.box_env, hidden_sizes=(64, 64), name='P4')
self.sess.run(tf.compat.v1.global_variables_initializer())
for a, b in zip(self.policy3.get_params(),
self.policy1.get_params()):
self.sess.run(a.assign(b))
for a, b in zip(self.policy4.get_params(),
self.policy2.get_params()):
self.sess.run(a.assign(b))
self.obs = self.box_env.reset()
self.action_bound = self.box_env.action_space.high
assert self.policy1.vectorized == self.policy2.vectorized
assert self.policy3.vectorized == self.policy4.vectorized
@mock.patch('numpy.random.normal')
def test_get_action(self, mock_rand):
mock_rand.return_value = 0.5
action1, _ = self.policy1.get_action(self.obs)
action2, _ = self.policy2.get_action(self.obs)
action3, _ = self.policy3.get_action(self.obs)
action4, _ = self.policy4.get_action(self.obs)
assert np.array_equal(action1, action3 * self.action_bound)
assert np.array_equal(action2, action4 * self.action_bound)
actions1, _ = self.policy1.get_actions([self.obs, self.obs])
actions2, _ = self.policy2.get_actions([self.obs, self.obs])
actions3, _ = self.policy3.get_actions([self.obs, self.obs])
actions4, _ = self.policy4.get_actions([self.obs, self.obs])
assert np.array_equal(actions1, actions3 * self.action_bound)
assert np.array_equal(actions2, actions4 * self.action_bound)
def test_get_action_sym(self):
obs_dim = self.box_env.spec.observation_space.flat_dim
state_input = tf.compat.v1.placeholder(
tf.float32, shape=(None, obs_dim))
action_sym1 = self.policy1.get_action_sym(
state_input, name='action_sym')
action_sym2 = self.policy2.get_action_sym(
state_input, name='action_sym')
action_sym3 = self.policy3.get_action_sym(
state_input, name='action_sym')
action_sym4 = self.policy4.get_action_sym(
state_input, name='action_sym')
action1 = self.sess.run(
action_sym1, feed_dict={state_input: [self.obs]})
action2 = self.sess.run(
action_sym2, feed_dict={state_input: [self.obs]})
action3 = self.sess.run(
action_sym3, feed_dict={state_input: [self.obs]})
action4 = self.sess.run(
action_sym4, feed_dict={state_input: [self.obs]})
assert np.array_equal(action1, action3 * self.action_bound)
assert np.array_equal(action2, action4 * self.action_bound)
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)
env.reset()
with LocalTFRunner(snapshot_config) as runner:
env = TfEnv(normalize(env))
action_noise = OUStrategy(env.spec, sigma=params['sigma'])
policy = ContinuousMLPPolicyWithModel(
env_spec=env.spec,
hidden_sizes=params['policy_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh,
input_include_goal=True,
)
qf = ContinuousMLPQFunction(
env_spec=env.spec,
hidden_sizes=params['qf_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu,
input_include_goal=True,
)
replay_buffer = HerReplayBuffer(
env_spec=env.spec,
size_in_transitions=params['replay_buffer_size'],
time_horizon=params['n_rollout_steps'],
replay_k=0.4,
reward_fun=env.compute_reward,
)
algo = DDPG(
env_spec=env.spec,
policy=policy,
qf=qf,
replay_buffer=replay_buffer,
policy_lr=params['policy_lr'],
qf_lr=params['qf_lr'],
target_update_tau=params['tau'],
n_train_steps=params['n_train_steps'],
discount=params['discount'],
exploration_strategy=action_noise,
policy_optimizer=tf.train.AdamOptimizer,
qf_optimizer=tf.train.AdamOptimizer,
buffer_batch_size=256,
input_include_goal=True,
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
logger.add_output(dowel.StdOutput())
logger.add_output(dowel.CsvOutput(tabular_log_file))
logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=params['n_epochs'],
n_epoch_cycles=params['n_epoch_cycles'],
batch_size=params['n_rollout_steps'])
logger.remove_all()
return tabular_log_file
