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 step(self, action_n):
results = singleton_pool.run_each(
worker_run_step,
[(action_n, self.scope) for _ in self._alloc_env_ids],
)
results = [x for x in results if x is not None]
ids, obs, rewards, dones, env_infos = list(zip(*results))
ids = np.concatenate(ids)
obs = self.observation_space.unflatten_n(np.concatenate(obs))
rewards = np.concatenate(rewards)
dones = np.concatenate(dones)
env_infos = tensor_utils.split_tensor_dict_list(tensor_utils.concat_tensor_dict_list(env_infos))
if env_infos is None:
env_infos = [dict() for _ in range(self.num_envs)]
items = list(zip(ids, obs, rewards, dones, env_infos))
items = sorted(items, key=lambda x: x[0])
ids, obs, rewards, dones, env_infos = list(zip(*items))
obs = list(obs)
rewards = np.asarray(rewards)
dones = np.asarray(dones)
self.ts += 1
dones[self.ts >= self.max_path_length] = True
reset_obs = self._run_reset(dones)
for (i, done) in enumerate(dones):
if done:
obs[i] = reset_obs[i]
self.ts[i] = 0
return obs, rewards, dones, tensor_utils.stack_tensor_dict_list(list(env_infos))
def __init__(self, env, n, max_path_length, scope=None):
if scope is None:
# initialize random scope
scope = str(uuid.uuid4())
envs_per_worker = int(np.ceil(n * 1.0 / singleton_pool.n_parallel))
alloc_env_ids = []
rest_alloc = n
start_id = 0
for _ in range(singleton_pool.n_parallel):
n_allocs = min(envs_per_worker, rest_alloc)
alloc_env_ids.append(list(range(start_id, start_id + n_allocs)))
start_id += n_allocs
rest_alloc = max(0, rest_alloc - envs_per_worker)
singleton_pool.run_each(worker_init_envs, [(alloc, scope, env) for alloc in alloc_env_ids])
self._alloc_env_ids = alloc_env_ids
self._action_space = env.action_space
self._observation_space = env.observation_space
self._num_envs = n
self.scope = scope
self.ts = np.zeros(n, dtype='int')
self.max_path_length = max_path_length
def sample_paths(
policy_params,
max_samples,
max_path_length=np.inf,
env_params=None,
scope=None,
reset_arg=None,
show_prog_bar=True,
multi_task=False):
"""
: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
"""
if multi_task:
assert len(policy_params) == singleton_pool.n_parallel
all_params = [(params, scope) for params in policy_params]
singleton_pool.run_each(
_worker_set_policy_params,
all_params,
)
else:
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
)
if multi_task:
args = [(max_path_length, scope, arg) for arg in reset_arg]
return singleton_pool.run_collect(
_worker_collect_one_path,
threshold=max_samples,
args=args,
show_prog_bar=show_prog_bar,
multi_task=multi_task,
)
else:
return singleton_pool.run_collect(
_worker_collect_one_path,
threshold=max_samples,
args=(max_path_length, scope, reset_arg),
show_prog_bar=show_prog_bar,
multi_task=multi_task,
)
def _run_reset(self, dones):
dones = np.asarray(dones)
results = singleton_pool.run_each(
worker_run_reset,
[(dones, self.scope) for _ in self._alloc_env_ids],
)
ids, flat_obs = list(map(np.concatenate, list(zip(*results))))
zipped = list(zip(ids, flat_obs))
sorted_obs = np.asarray([x[1] for x in sorted(zipped, key=lambda x: x[0])])
done_ids, = np.where(dones)
done_flat_obs = sorted_obs[done_ids]
done_unflat_obs = self.observation_space.unflatten_n(done_flat_obs)
all_obs = [None] * self.num_envs
done_cursor = 0
for idx, done in enumerate(dones):
if done:
all_obs[idx] = done_unflat_obs[done_cursor]
done_cursor += 1
return all_obs
def start_worker(self):
if singleton_pool.n_parallel > 1:
singleton_pool.run_each(worker_init_tf)
parallel_sampler.populate_task(self.algo.env, self.algo.policy)
if singleton_pool.n_parallel > 1:
singleton_pool.run_each(worker_init_tf_vars)
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)])