def populate_task(env, policy, scope=None):
logger.log("Populating workers...")
if singleton_pool.n_parallel > 1:
singleton_pool.run_each(
_worker_populate_task,
[(pickle.dumps(env), pickle.dumps(policy), scope)] *
singleton_pool.n_parallel)
else:
# avoid unnecessary copying
G = _get_scoped_G(singleton_pool.G, scope)
G.env = env
G.policy = policy
logger.log("Populated")
def populate_task(env, policy, ma_mode, scope=None):
logger.log("Populating workers...")
logger.log("ma_mode={}".format(ma_mode))
if singleton_pool.n_parallel > 1:
singleton_pool.run_each(
_worker_populate_task,
[(pickle.dumps(env), pickle.dumps(policy), ma_mode, scope)] *
singleton_pool.n_parallel)
else:
# avoid unnecessary copying
G = _get_scoped_G(singleton_pool.G, scope)
G.env = env
if ma_mode == 'concurrent':
G.policies = policy
else:
G.policy = policy
logger.log("Populated")
def sample_paths(policy_params,
max_samples,
ma_mode,
max_path_length=np.inf,
env_params=None,
scope=None):
if ma_mode == 'concurrent':
assert isinstance(policy_params, list)
singleton_pool.run_each(_worker_set_policy_params,
[(policy_params, ma_mode, scope)] *
singleton_pool.n_parallel)
if env_params is not None:
singleton_pool.run_each(_worker_set_env_params, [(env_params, scope)] *
singleton_pool.n_parallel)
return singleton_pool.run_collect(_worker_collect_path_one_env,
threshold=max_samples,
args=(max_path_length, ma_mode, scope),
show_prog_bar=True)
def sample_paths(policy_params,
max_samples,
max_path_length=np.inf,
env_params=None,
scope=None):
"""
:param policy_params: parameters for the policy. This will be updated on each worker process
:param max_samples: desired maximum number of samples to be collected. The actual number of collected samples
might be greater since all trajectories will be rolled out either until termination or until max_path_length is
reached
:param max_path_length: horizon / maximum length of a single trajectory
:return: a list of collected paths
"""
singleton_pool.run_each(_worker_set_policy_params,
[(policy_params, scope)] *
singleton_pool.n_parallel)
if env_params is not None:
singleton_pool.run_each(_worker_set_env_params, [(env_params, scope)] *
singleton_pool.n_parallel)
return singleton_pool.run_collect(_worker_collect_one_path,
threshold=max_samples,
args=(max_path_length, scope),
show_prog_bar=True)
def set_seed(seed):
singleton_pool.run_each(_worker_set_seed,
[(seed + i, )
for i in range(singleton_pool.n_parallel)])
def terminate_task(scope=None):
singleton_pool.run_each(_worker_terminate_task,
[(scope, )] * singleton_pool.n_parallel)
def initialize(n_parallel):
singleton_pool.initialize(n_parallel)
singleton_pool.run_each(_worker_init,
[(id, )
for id in range(singleton_pool.n_parallel)])