class TestLinearFeatureBaseline(unittest.TestCase):
def setUp(self):
self.random_env = RandomEnv()
self.random_policy = RandomPolicy(1, 1)
self.meta_batch_size = 2
self.batch_size = 10
self.path_length = 100
self.linear = LinearFeatureBaseline()
self.sampler = MetaSampler(self.random_env,
self.random_policy,
self.batch_size,
self.meta_batch_size,
self.path_length,
parallel=True)
def testFit(self):
paths = self.sampler.obtain_samples()
for task in paths.values():
unfit_error = 0
for path in task:
path["returns"] = utils.discount_cumsum(path["rewards"], 0.99)
unfit_pred = self.linear.predict(path)
unfit_error += sum([
np.square(pred - actual)
for pred, actual in zip(unfit_pred, path['returns'])
])
self.linear.fit(task)
fit_error = 0
for path in task:
fit_pred = self.linear.predict(path)
fit_error += sum([
np.square(pred - actual)
for pred, actual in zip(fit_pred, path['returns'])
])
self.assertTrue(fit_error < unfit_error)
def testSerialize(self):
paths = self.sampler.obtain_samples()
for task in paths.values():
for path in task:
path["returns"] = utils.discount_cumsum(path["rewards"], 0.99)
self.linear.fit(task)
fit_error_pre = 0
for path in task:
fit_pred = self.linear.predict(path)
fit_error_pre += sum([
np.square(pred - actual)
for pred, actual in zip(fit_pred, path['returns'])
])
pkl = pickle.dumps(self.linear)
self.linear = pickle.loads(pkl)
fit_error_post = 0
for path in task:
fit_pred = self.linear.predict(path)
fit_error_post += sum([
np.square(pred - actual)
for pred, actual in zip(fit_pred, path['returns'])
])
self.assertEqual(fit_error_pre, fit_error_post)
class TestLikelihoodRation(unittest.TestCase):
"""
Assure that likelihhood ratio at first gradient step is approx. one since pi_old = pi_new
"""
def setUp(self):
self.env = env = MetaPointEnv()
self.baseline = baseline = LinearFeatureBaseline()
self.policy = policy = MetaGaussianMLPPolicy(
name="meta-policy",
obs_dim=np.prod(env.observation_space.shape),
action_dim=np.prod(env.action_space.shape),
meta_batch_size=10,
hidden_sizes=(16, 16),
learn_std=True,
hidden_nonlinearity=tf.tanh,
output_nonlinearity=None,
)
self.sampler = MetaSampler(
env=env,
policy=policy,
rollouts_per_meta_task=2,
meta_batch_size=10,
max_path_length=50,
parallel=False,
)
self.sample_processor = MetaSampleProcessor(
baseline=baseline,
discount=0.99,
gae_lambda=1.0,
normalize_adv=True,
positive_adv=False,
)
self.algo = ProMP(
policy=policy,
inner_lr=0.1,
meta_batch_size=10,
num_inner_grad_steps=2,
learning_rate=1e-3,
num_ppo_steps=5,
num_minibatches=1,
clip_eps=0.5,
target_inner_step=2e-2,
init_inner_kl_penalty=1e-3,
)
def test_likelihood_ratio(self):
with tf.Session() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
self.sampler.update_tasks()
self.policy.switch_to_pre_update() # Switch to pre-update policy
all_samples_data, all_paths = [], []
for step in range(1):
""" -------------------- Sampling --------------------------"""
paths = self.sampler.obtain_samples(log_prefix=str(step))
all_paths.append(paths)
""" ----------------- Processing Samples ---------------------"""
samples_data = self.sample_processor.process_samples(paths,
log=False)
all_samples_data.append(samples_data)
""" ------------------- Inner Policy Update --------------------"""
obs_phs, action_phs, adv_phs, dist_info_phs, all_phs = self.algo._make_input_placeholders(
'')
for i in range(self.algo.meta_batch_size):
obs = samples_data[i]['observations']
actions = samples_data[i]['actions']
agent_infos = samples_data[i]['agent_infos']
param_vals = self.policy.get_param_values()
likelihood_ratio_sym = self.policy.likelihood_ratio_sym(
obs_phs[i], action_phs[i], dist_info_phs[i],
self.policy.policies_params_phs[i])
feed_dict_params = dict(
zip(self.policy.policies_params_phs[i].values(),
param_vals.values()))
feed_dict_dist_infos = dict(
zip(dist_info_phs[i].values(), agent_infos.values()))
feed_dict = {obs_phs[i]: obs, action_phs[i]: actions}
feed_dict.update(feed_dict_params)
feed_dict.update(feed_dict_dist_infos)
lr = sess.run(likelihood_ratio_sym, feed_dict=feed_dict)
self.assertTrue(np.allclose(lr, 1))
assert hasattr(
wrapped_env,
'dt'), 'environment must have dt attribute that specifies the timestep'
timestep = wrapped_env.dt
speedup = 1
with sess.as_default() as sess:
policy.switch_to_pre_update()
# Preupdate:
tasks = env.sample_tasks(params['meta_batch_size'])
sampler.vec_env.set_tasks(tasks)
# Preupdate:
for i in range(params['num_inner_grad_steps']):
paths = sampler.obtain_samples(log=False)
samples_data = sample_processor.process_samples(paths,
log=True,
log_prefix='%i_' %
i)
env.log_diagnostics(sum(list(paths.values()), []),
prefix='%i_' % i)
algo._adapt(samples_data)
paths = sampler.obtain_samples(log=False)
samples_data = sample_processor.process_samples(
paths, log=True, log_prefix='%i_' % params['num_inner_grad_steps'])
env.log_diagnostics(sum(list(paths.values()), []),
prefix='%i_' % params['num_inner_grad_steps'])
logger.dumpkvs()
images = []
