def make_env(scenario_name, benchmark=False):
from multiagent.environment_uav import MultiAgentEnv
import multiagent.scenarios as scenarios
scenario = scenarios.load(scenario_name + ".py").Scenario()
world = scenario.make_world()
if benchmark:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward,
scenario.observation, scenario.benchmark_data)
else:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward,
scenario.observation)
return env
def make_env(scenario_name, arglist, benchmark=False):
from multiagent.environment_uav import MultiAgentEnv
import multiagent.scenarios as scenarios
# load scenario from script
scenario = scenarios.load(scenario_name + ".py").Scenario()
# create world
world = scenario.make_world()
# create multiagent environment
if benchmark:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, scenario.benchmark_data)
else:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
return env
# parse arguments
parser = argparse.ArgumentParser(description=None)
parser.add_argument('-s',
'--scenario',
default='simple.py',
help='Path of the scenario Python script.')
args = parser.parse_args()
# load scenario from script
scenario = scenarios.load(args.scenario).Scenario()
# create world
world = scenario.make_world()
# create multiagent environment
env = MultiAgentEnv(world,
scenario.reset_world,
scenario.reward,
scenario.observation,
info_callback=None,
shared_viewer=False)
# render call to create viewer window (necessary only for interactive policies)
env.render()
# create interactive policies for each agent
policies = [InteractivePolicy(env, i) for i in range(env.n)]
# execution loop
obs_n = env.reset()
while True:
# query for action from each agent's policy
act_n = []
for i, policy in enumerate(policies):
act_n.append(policy.action(obs_n[i]))
# step environment
obs_n, reward_n, done_n, _ = env.step(act_n)