def create_env(env, seed):
if env.startswith('rllab.'):
# Get env name and class
env_name = re.match('rllab.(\S+)', env).group(1)
env_rllab_class = rllab_env_from_name(env_name)
# Define env maker
def make_env():
env_rllab = env_rllab_class()
_env = Rllab2GymWrapper(env_rllab)
return _env
# Used later
env_type = 'rllab'
else:
# Normal gym, get if Atari or not.
env_type = get_env_type(env)
assert env_type is not None, "Env not recognized."
# Define the correct env maker
if env_type == 'atari':
# Atari, custom env creation
def make_env():
_env = make_atari(env)
return wrap_deepmind(_env)
else:
# Not atari, standard env creation
def make_env():
env_rllab = gym.make(env)
return env_rllab
env = make_env()
env.seed(seed)
return env
def train(env, policy, seed, njobs=1, **alg_args):
if env.startswith('rllab.'):
# Get env name and class
env_name = re.match('rllab.(\w+)', env).group(1)
env_rllab_class = rllab_env_from_name(env_name)
# Define env maker
def make_env(seed=0):
def _thunk():
env_rllab = Rllab2GymWrapper(env_rllab_class())
env_rllab.seed(seed)
return env_rllab
return _thunk
parallel_env = SubprocVecEnv([make_env(i + seed) for i in range(njobs)])
# Used later
env_type = 'rllab'
else:
# Normal gym, get if Atari or not.
env_type = get_env_type(env)
assert env_type is not None, "Env not recognized."
# Define the correct env maker
if env_type == 'atari':
# Atari, custom env creation
def make_env(seed=0):
def _thunk():
_env = make_atari(env)
_env.seed(seed)
return wrap_deepmind(_env)
return _thunk
parallel_env = VecFrameStack(SubprocVecEnv([make_env(i + seed) for i in range(njobs)]), 4)
else:
# Not atari, standard env creation
def make_env(seed=0):
def _thunk():
_env = gym.make(env)
_env.seed(seed)
return _env
return _thunk
parallel_env = SubprocVecEnv([make_env(i + seed) for i in range(njobs)])
# Create the policy
if policy == 'linear':
hid_size = num_hid_layers = 0
elif policy == 'nn':
hid_size = [100, 50, 25]
num_hid_layers = 3
if policy == 'linear' or policy == 'nn':
def make_policy(name, ob_space, ac_space):
return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=hid_size, num_hid_layers=num_hid_layers, gaussian_fixed_var=True, use_bias=False, use_critic=False,
hidden_W_init=tf.contrib.layers.xavier_initializer(),
output_W_init=tf.contrib.layers.xavier_initializer())
elif policy == 'cnn':
def make_policy(name, ob_space, ac_space):
return CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
gaussian_fixed_var=True, use_bias=False, use_critic=False,
hidden_W_init=tf.contrib.layers.xavier_initializer(),
output_W_init=tf.contrib.layers.xavier_initializer())
else:
raise Exception('Unrecognized policy type.')
try:
affinity = len(os.sched_getaffinity(0))
except:
affinity = njobs
sess = U.make_session(affinity)
sess.__enter__()
set_global_seeds(seed)
gym.logger.setLevel(logging.WARN)
pois2.learn(parallel_env, make_policy, **alg_args)
def train(env, policy, policy_init, n_episodes, horizon, seed, njobs=1, save_weights=False, **alg_args):
if env.startswith('rllab.'):
# Get env name and class
env_name = re.match('rllab.(\S+)', env).group(1)
env_rllab_class = rllab_env_from_name(env_name)
# Define env maker
def make_env():
env_rllab = env_rllab_class()
_env = Rllab2GymWrapper(env_rllab)
return _env
# Used later
env_type = 'rllab'
else:
# Normal gym, get if Atari or not.
env_type = get_env_type(env)
assert env_type is not None, "Env not recognized."
# Define the correct env maker
if env_type == 'atari':
# Atari, custom env creation
def make_env():
_env = make_atari(env)
return wrap_deepmind(_env)
else:
# Not atari, standard env creation
def make_env():
env_rllab = gym.make(env)
return env_rllab
if policy == 'linear':
hid_size = num_hid_layers = 0
elif policy == 'nn':
hid_size = [100, 50, 25]
num_hid_layers = 3
if policy_init == 'xavier':
policy_initializer = tf.contrib.layers.xavier_initializer()
elif policy_init == 'zeros':
policy_initializer = U.normc_initializer(0.0)
else:
raise Exception('Unrecognized policy initializer.')
if policy == 'linear' or policy == 'nn':
def make_policy(name, ob_space, ac_space):
return MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=hid_size, num_hid_layers=num_hid_layers, gaussian_fixed_var=True, use_bias=False, use_critic=False,
hidden_W_init=policy_initializer, output_W_init=policy_initializer)
elif policy == 'cnn':
def make_policy(name, ob_space, ac_space):
return CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
gaussian_fixed_var=True, use_bias=False, use_critic=False,
hidden_W_init=policy_initializer,
output_W_init=policy_initializer)
else:
raise Exception('Unrecognized policy type.')
sampler = ParallelSampler(make_policy, make_env, n_episodes, horizon, True, n_workers=njobs, seed=seed)
try:
affinity = len(os.sched_getaffinity(0))
except:
affinity = njobs
sess = U.make_session(affinity)
sess.__enter__()
set_global_seeds(seed)
gym.logger.setLevel(logging.WARN)
pois.learn(make_env, make_policy, n_episodes=n_episodes, horizon=horizon,
sampler=sampler, save_weights=save_weights, **alg_args)
sampler.close()
def create_sampler(env=None,
policy='linear',
n_episodes=100,
horizon=500,
njobs=1,
seed=42):
# Create the environment
if env.startswith('rllab.'):
# Get env name and class
env_name = re.match('rllab.(\S+)', env).group(1)
env_rllab_class = rllab_env_from_name(env_name)
# Define env maker
def make_env():
env_rllab = env_rllab_class()
_env = Rllab2GymWrapper(env_rllab)
return _env
# Used later
env_type = 'rllab'
else:
# Normal gym, get if Atari or not.
env_type = get_env_type(env)
assert env_type is not None, "Env not recognized."
# Define the correct env maker
if env_type == 'atari':
# Atari, custom env creation
def make_env():
_env = make_atari(env)
return wrap_deepmind(_env)
else:
# Not atari, standard env creation
def make_env():
env_rllab = gym.make(env)
return env_rllab
# Select policy architecture
if policy == 'linear':
hid_size = num_hid_layers = 0
use_bias = False
elif policy == 'simple-nn':
hid_size = [16]
num_hid_layers = 1
use_bias = True
elif policy == 'nn':
hid_size = [100, 50, 25]
num_hid_layers = 3
use_bias = True
policy_initializer = U.normc_initializer(0.0)
if policy == 'linear' or policy == 'nn' or policy == 'simple-nn':
def make_policy(name, ob_space, ac_space):
return MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
hid_size=hid_size,
num_hid_layers=num_hid_layers,
gaussian_fixed_var=True,
use_bias=use_bias,
use_critic=False,
hidden_W_init=policy_initializer,
output_W_init=policy_initializer)
elif policy == 'cnn':
def make_policy(name, ob_space, ac_space):
return CnnPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
gaussian_fixed_var=True,
use_bias=False,
use_critic=False,
hidden_W_init=policy_initializer,
output_W_init=policy_initializer)
else:
raise Exception('Unrecognized policy type.')
# Create the sampler
sampler = ParallelSampler(make_policy,
make_env,
n_episodes,
horizon,
True,
n_workers=njobs,
seed=seed)
try:
affinity = len(os.sched_getaffinity(0))
except:
affinity = njobs
sess = U.make_session(affinity)
sess.__enter__()
# Set random seed
set_global_seeds(seed)
return sampler
