def SendActionResult(self, action_result, context):
"""Receive ActionResult proto and send back an action."""
self.queues['action_response'].put(action_result)
if not action_result.done:
try:
action = self.queues['action'].get(timeout=10000.0)
except queue.Empty:
action = ExternalControl_pb2.Action(done=True)
res = ExternalControl_pb2.ActionResult(done=True)
self.queues['action_response'].put(res)
else:
action = ExternalControl_pb2.Action(done=True)
res = ExternalControl_pb2.ActionResult(done=True)
self.queues['action_response'].put(res)
return action
def _return_action_result(self, index):
info = {}
try:
res = self.queues[index]['action_response'].get(timeout=60)
except queue.Empty:
print('Scrimmage Environment: error getting action result')
res = ExternalControl_pb2.ActionResult(done=True)
info['scrimmage_err'] = ""
obs = np.array(res.observations.value)
return obs, res.reward, res.done, {}
def main():
# Get params file, model:class, and port num from args list
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--xml', default="/tmp/p", help='Parameters file path')
parser.add_argument('--actor', type=str,
help='what actor to use for acting. '
'Input in the form module:Class (e.g., '
'load_model:NNModel) and should have the same basic '
'methods/properties as load_model:NNModel')
parser.add_argument("--port-num", type=int, default=50052,
help="port to start scrimmage interface on")
args = parser.parse_args()
# Initizialized variables
num_actors = 1
port_offset = 1
queue_names = ['env', 'action', 'action_response']
ip = "localhost"
queues = []
server_threads = []
port = args.port_num
# Starting up GRPC link
for i in range(num_actors):
port = int(port) + i * port_offset
address = ip + ":" + str(port)
queues.append({s: queue.Queue() for s in queue_names})
server_threads.append(
ServerThread(queues[-1], address))
server_threads[-1].start()
environments = \
[queues[i]['env'].get() for i in range(num_actors)]
if len(environments) == 1:
action_space = _create_tuple_space(environments[0].action_spaces)
observation_space = _create_tuple_space(
environments[0].observation_spaces)
else:
action_space = [_create_tuple_space(e.action_spaces)for e in
environments]
observation_space = [_create_tuple_space(e.observation_spaces) for e in
environments]
action_space = gym.spaces.Tuple(action_space)
observation_space = gym.spaces.Tuple(observation_space)
# Set up actor model
actor_module_str, actor_class_str = args.actor.split(":")
actor_module = importlib.import_module(actor_module_str)
actor_class = getattr(actor_module, actor_class_str)
# Actor initialization
actor = actor_class(action_space, observation_space, args.xml)
while True:
# get state from GRPC link
try:
res = queues[0]['action_response'].get(timeout=60)
except queue.Empty:
print('Not receiving sensor info from scrimmage')
res = ExternalControl_pb2.ActionResult(done=True)
if res.done:
print("Received end of simulation")
for s in server_threads:
s.stop = True
break
obs = np.array(res.observations.value)
# get action from actor
action = actor.act(obs)
# return action to GRPC link
def _step_single(i, space, a):
if not isinstance(a, collections.Iterable):
a = [a]
if isinstance(action_space, gym.spaces.Box):
action_pb =\
ExternalControl_pb2.Action(continuous=a, done=False)
else:
action_pb =\
ExternalControl_pb2.Action(discrete=a, done=False)
queues[i]['action'].put(action_pb)
if num_actors == 1:
_step_single(0, action_space, action)
else:
for i, a in enumerate(action):
_step_single(i, action_space.spaces[i], a)