def init_plot(self, env, policy):
if not Plotter.enable:
return
if not (self._process and self._queue):
self.init_worker()
# Needed in order to draw glfw window on the main thread
if ('Darwin' in platform.platform()):
rollout(env,
policy,
max_path_length=np.inf,
animated=True,
speedup=5)
self._queue.put(Message(op=Op.UPDATE, args=(env, policy), kwargs=None))
def _obtain_evaluation_samples(self,
env,
num_trajs=100,
max_path_length=1000):
"""Sample the policy for 10 trajectories and return average values.
Args:
env (metarl.envs.MetaRLEnv): The environement used to obtain
trajectories.
num_trajs (int): Number of trajectories.
max_path_length (int): Number of maximum steps in one batch.
Returns:
TrajectoryBatch: Evaluation trajectories, representing the best
current performance of the algorithm.
"""
paths = []
for _ in range(num_trajs):
path = rollout(env,
self.policy,
max_path_length=max_path_length,
deterministic=True)
paths.append(path)
return TrajectoryBatch.from_trajectory_list(self.env_spec, paths)
def _worker_start(self):
env = None
policy = None
max_length = None
initial_rollout = True
try:
# Each iteration will process ALL messages currently in the
# queue
while True:
msgs = {}
# If true, block and yield processor
if initial_rollout:
msg = self._queue.get()
msgs[msg.op] = msg
# Only fetch the last message of each type
while not self._queue.empty():
msg = self._queue.get()
msgs[msg.op] = msg
else:
# Only fetch the last message of each type
while not self._queue.empty():
msg = self._queue.get_nowait()
msgs[msg.op] = msg
if Op.STOP in msgs:
break
elif Op.UPDATE in msgs:
env, policy = msgs[Op.UPDATE].args
elif Op.DEMO in msgs:
param_values, max_length = msgs[Op.DEMO].args
policy.set_param_values(param_values)
initial_rollout = False
rollout(env,
policy,
max_path_length=max_length,
animated=True,
speedup=5)
else:
if max_length:
rollout(env,
policy,
max_path_length=max_length,
animated=True,
speedup=5)
except KeyboardInterrupt:
pass
def test_max_path_length(self):
# pylint: disable=unsubscriptable-object
path = utils.rollout(self.env, self.policy, max_path_length=3)
assert path['observations'].shape[0] == 3
assert path['actions'].shape[0] == 3
assert path['rewards'].shape[0] == 3
agent_info = [
path['agent_infos'][k]
for k in self.policy.distribution.dist_info_keys
]
assert agent_info[0].shape[0] == 3
# dummy is the env_info_key
assert path['env_infos']['dummy'].shape[0] == 3
def _worker_collect_one_path(g, max_path_length, scope=None):
g = _get_scoped_g(g, scope)
path = rollout(g.env, g.policy, max_path_length=max_path_length)
return path, len(path['rewards'])
def test_does_flatten(self):
path = utils.rollout(self.env, self.policy, max_path_length=5)
assert path['observations'][0].shape == (16, )
assert path['actions'][0].shape == (2, 2)
"(or 'y' or 'n').\n")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('file', type=str, help='path to the snapshot file')
parser.add_argument('--max_path_length',
type=int,
default=1000,
help='Max length of rollout')
parser.add_argument('--speedup', type=float, default=1, help='Speedup')
args = parser.parse_args()
# If the snapshot file use tensorflow, do:
# import tensorflow as tf
# with tf.compat.v1.Session():
# [rest of the code]
with tf.compat.v1.Session() as sess:
data = joblib.load(args.file)
policy = data['algo'].policy
env = data['env']
while True:
path = rollout(env,
policy,
max_path_length=args.max_path_length,
animated=True,
speedup=args.speedup)
if not query_yes_no('Continue simulation?'):
break