def make_env(scenario_name, benchmark=False, pre_encode=False):
'''
Creates a MultiAgentEnv object as env. This can be used similar to a gym
environment by calling env.reset() and env.step().
Use env.render() to view the environment on the screen.
Input:
scenario_name : name of the scenario from ./scenarios/ to be Returns
(without the .py extension)
'''
from envs.environment import MultiAgentEnv
import envs.scenarios as scenarios
# load scenario from script
scenario = scenarios.load(scenario_name + ".py").Scenario()
# create world
world = scenario.make_world()
# create multiagent environment
env = MultiAgentEnv(world,
reset_callback=scenario.reset_world,
reward_callback=scenario.reward,
observation_callback=scenario.observation,
done_callback=scenario.done,
info_callback=scenario.info,
rx_callback=scenario.received,
tx_callback=scenario.transmitted,
pre_encode=pre_encode)
return env
def make_env(scenario_name):
'''
Creates a MultiAgentEnv object as env. This can be used similar to a gym
environment by calling env.reset() and env.step().
Use env.render() to view the environment on the screen.
Input:
scenario_name : name of the scenario from ./scenarios/ to be Returns
(without the .py extension)
Some useful env properties (see environment.py):
.observation_space : Returns the observation space for each agent
.action_space : Returns the action space for each agent
.n : Returns the number of Agents
'''
from envs.environment import MultiAgentEnv
import envs.scenarios as scenarios
# load scenario from script
scenario = scenarios.load(scenario_name + ".py").Scenario()
# create world
world = scenario.make_world()
# create multiagent environment
env = MultiAgentEnv(world, reset_callback=scenario.reset_world,
reward_callback=scenario.reward,
observation_callback=scenario.observation,
info_callback=scenario.info,
done_callback=scenario.done,)
return env
def make_env(scenario_name, benchmark=False):
'''
Creates a MultiAgentEnv object as env. This can be used similar to a gym
environment by calling env.reset() and env.step().
Use env.render() to view the environment on the screen.
Input:
scenario_name : name of the scenario from ./scenarios/ to be Returns
(without the .py extension)
benchmark : whether you want to produce benchmarking data
(usually only done during evaluation)
Some useful env properties (see environment.py):
.observation_space : Returns the observation space for each agent
.action_space : Returns the action space for each agent
.n : Returns the number of Agents
'''
from envs.environment import MultiAgentEnv
import envs.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,
reset_callback=scenario.reset_world,
reward_callback=scenario.reward,
observation_callback=scenario.observation,
done_callback=scenario.done)
return env
def make_env(scenario_name, n_drone=1):
"""
:param scenario_name: name of the scenario from ./scenarios/ to be Returns
(without the .py extension)
:param n_drone: number of the drones in the world
:return:
"""
from envs.environment import Env
import envs.scenarios as scenarios
# load scenario from script
scenario = scenarios.load(scenario_name + ".py").Scenario()
# create world
world = scenario.make_world(n_drone, scenario.target_move)
# create Simsim environment
env = Env(world, scenario.reset_world,
scenario.get_reward_function(FLAGS.reward), scenario.observation,
scenario.info, scenario.done)
return env
import numpy as np
import config
FLAGS = config.flags.FLAGS
if __name__ == '__main__':
# parse arguments
parser = argparse.ArgumentParser(description=None)
parser.add_argument('-s',
'--scenario',
default='pursuit.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,
done_callback=scenario.done)
act_n = [2, 2]
print "action space:", env.action_space[0].n
print "observation space:", env.observation_space
obs_n = env.reset()[:2]
print env.get_agent_profile()
print env.get_full_encoding()[:, :, 2]