def test_log_float_param(self):
param = hp.LogFloatParam("variable", 1e-5, 1e-1)
n = 10000
num_success = 0
threshold = 1e-3
for _ in range(n):
if param.generate() > threshold:
num_success += 1
p = 0.5
self.assertTrue(is_binomial_trial_likely(n, p, num_success))
def main():
num_hyperparameters = 40
layer_norm = True
sweeper = hp.RandomHyperparameterSweeper([
hp.LogFloatParam("qf_learning_rate", 1e-5, 1e-1),
hp.LogFloatParam("policy_learning_rate", 1e-5, 1e-1),
hp.LogFloatParam("reward_scale", 10.0, 0.001),
hp.LogFloatParam("discount", 0.5, 0.99),
])
for seed in range(num_hyperparameters):
params_dict = sweeper.generate_random_hyperparameters()
variant = dict(
algo_params=dict(batch_size=128,
n_epochs=50,
epoch_length=1000,
eval_samples=1000,
replay_pool_size=1000000,
min_pool_size=256,
max_path_length=1000,
qf_weight_decay=0.00,
n_updates_per_time_step=5,
soft_target_tau=0.01,
**params_dict),
env_params=dict(
env_id='cart',
normalize_env=True,
gym_name="",
),
policy_params=dict(layer_norm=layer_norm, ),
qf_params=dict(layer_norm=layer_norm, ),
)
run_experiment(
my_ddpg_launcher,
exp_prefix="3-16-cartpole-ddpg-sweep-test",
seed=seed,
variant=variant,
mode="ec2",
)
def test_sweep_hyperparameters(self):
sweeper = hp.RandomHyperparameterSweeper([
hp.LinearFloatParam("v1", -10, 10),
hp.LogFloatParam("v2", 1e-5, 1e-1),
])
n = 100
num_successes = np.zeros((2, 2))
threshold_v1 = 0
threshold_v2 = 1e-3
def update_success(v1, v2):
success_v1 = int(v1 > threshold_v1)
success_v2 = int(v2 > threshold_v2)
num_successes[success_v1, success_v2] += 1
sweeper.sweep_hyperparameters(update_success, n)
p = 0.25
for i in range(2):
for j in range(2):
self.assertTrue(
is_binomial_trial_likely(n, p, num_successes[i, j]))
def get_launch_settings(algo_name):
"""
Return a dictionary of the form
{
'algo_params': algo_params to pass to run_algorithm
'variant': variant to pass to run_algorithm
}
:param algo_name: Name of the algorithm to run.
:return:
"""
sweeper = hp.RandomHyperparameterSweeper()
algo_params = {}
if algo_name == 'ddpg' or algo_name == 'mddpg':
sweeper = hp.RandomHyperparameterSweeper([
hp.LogFloatParam("qf_learning_rate", 1e-5, 1e-2),
hp.LogFloatParam("policy_learning_rate", 1e-6, 1e-3),
hp.LogFloatParam("reward_scale", 10.0, 0.001),
hp.LogFloatParam("soft_target_tau", 1e-5, 1e-2),
])
algo_params = get_ddpg_params()
algo_params['render'] = render
variant = {
'qf_params': dict(
embedded_hidden_sizes=(100,),
observation_hidden_sizes=(100,),
hidden_nonlinearity=tf.nn.relu,
),
'policy_params': dict(
observation_hidden_sizes=(100, 100),
hidden_nonlinearity=tf.nn.relu,
)
}
if algo_name == 'ddpg':
algorithm_launcher = my_ddpg_launcher
variant['Algorithm'] = 'DDPG'
variant['policy_params']['output_nonlinearity'] = tf.nn.tanh
else:
algorithm_launcher = mem_ddpg_launcher
variant['Algorithm'] = 'Memory-DDPG'
elif algo_name == 'naf':
sweeper = hp.RandomHyperparameterSweeper([
hp.LogFloatParam("qf_learning_rate", 1e-5, 1e-2),
hp.LogFloatParam("reward_scale", 10.0, 0.001),
hp.LogFloatParam("soft_target_tau", 1e-6, 1e-1),
hp.LogFloatParam("qf_weight_decay", 1e-7, 1e-1),
])
algo_params = get_my_naf_params()
algo_params['render'] = render
algorithm_launcher = naf_launcher
variant = {
'Algorithm': 'NAF',
'exploration_strategy_params': {
'sigma': 0.15
},
}
elif algo_name == 'random':
algorithm_launcher = random_action_launcher
variant = {'Algorithm': 'Random'}
elif algo_name == 'bptt':
algorithm_launcher = bptt_launcher
variant = {'Algorithm': 'BPTT'}
else:
raise Exception("Algo name not recognized: " + algo_name)
# bn_sweeper = hp.RandomHyperparameterSweeper([
# hp.EnumParam("decay", [0.9, 0.99, 0.999, 0.9999]),
# hp.LogFloatParam("epsilon", 1e-3, 1e-7),
# hp.EnumParam("enable_offset", [True, False]),
# hp.EnumParam("enable_scale", [True, False]),
# ])
bn_sweeper = None
return {
'sweeper': sweeper,
'batch_norm_sweeper': bn_sweeper,
'variant': variant,
'algo_params': algo_params,
'algorithm_launcher': algorithm_launcher,
'batch_norm_params': BATCH_NORM_PARAMS
}
seed = random.randint(0, 10000)
run_experiment(
experiment,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,
exp_id=exp_id,
sync_s3_log=True,
sync_s3_pkl=True,
periodic_sync_interval=600,
)
if run_mode == 'random':
hyperparameters = [
hyp.LinearFloatParam('algo_params.discount', 0, 1),
hyp.LogFloatParam('algo_params.policy_learning_rate', 1e-7, 1e-1),
hyp.LogFloatParam('algo_params.qf_learning_rate', 1e-7, 1e-1),
hyp.LogIntParam('algo_params.target_hard_update_period', 1, 1000),
]
sweeper = hyp.RandomHyperparameterSweeper(
hyperparameters,
default_kwargs=variant,
)
for _ in range(num_configurations):
for exp_id in range(n_seeds):
seed = random.randint(0, 10000)
variant = sweeper.generate_random_hyperparameters()
run_experiment(
experiment,
exp_prefix=exp_prefix,
seed=seed,
)
if run_mode == 'random':
for (
rnn_cell,
output_activation,
) in [
(LSTMCell, F.tanh),
(LSTMCell, ptu.clip1),
(GRUCell, F.tanh),
(GRUCell, ptu.clip1),
]:
variant['policy_params']['cell_class'] = rnn_cell
variant['policy_params']['output_activation'] = output_activation
hyperparameters = [
hyp.LogIntParam('memory_dim', 4, 400),
hyp.LogFloatParam('algo_params.qf_learning_rate', 1e-5, 1e-2),
hyp.LogFloatParam(
'algo_params.write_policy_learning_rate', 1e-5, 1e-3
),
hyp.LogFloatParam(
'algo_params.action_policy_learning_rate', 1e-5, 1e-3
),
# hyp.EnumParam(
# 'algo_params.action_policy_optimize_bellman', [True, False],
# ),
# hyp.EnumParam(
# 'algo_params.use_action_policy_params_for_entire_policy',
# [True, False],
# ),
# hyp.EnumParam(
# 'algo_params.write_policy_optimizes', ['both', 'qf', 'bellman']
n_seeds,
experiment,
exp_prefix=exp_prefix,
),
search_space=search_space,
extra_function_kwargs=variant,
maximize=True,
verbose=True,
load_trials=True,
num_rounds=500,
num_evals_per_round=1,
)
elif run_mode == 'random':
hyperparameters = [
hyp.LinearFloatParam('foo', 0, 1),
hyp.LogFloatParam('bar', 1e-5, 1e2),
]
sweeper = hyp.RandomHyperparameterSweeper(
hyperparameters,
default_kwargs=variant,
)
for _ in range(num_configurations):
for exp_id in range(n_seeds):
seed = random.randint(0, 10000)
variant = sweeper.generate_random_hyperparameters()
run_experiment(
experiment,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,