def test_init(self):
# Initialize a pyglet renderer
self.renderer = Renderer(self.network,
mode=self.mode,
save_render=self.save_render,
sight_radius=self.sight_radius,
pxpm=self.pxpm,
show_radius=self.show_radius,
alpha=self.alpha)
# Ensure that the attributes match their correct values
self.assertEqual(self.renderer.mode, self.mode)
self.assertEqual(self.renderer.save_render, self.save_render)
self.assertEqual(self.renderer.sight_radius, self.sight_radius)
self.assertEqual(self.renderer.pxpm, self.pxpm)
self.assertEqual(self.renderer.show_radius, self.show_radius)
self.assertEqual(self.renderer.alpha, self.alpha)
def test_render_drgb(self):
# Initialize a pyglet renderer
self.renderer = Renderer(self.network,
mode=self.mode,
save_render=self.save_render,
sight_radius=self.sight_radius,
pxpm=self.pxpm,
show_radius=self.show_radius,
alpha=self.alpha)
_human_orientations, _machine_orientations, \
_human_dynamics, _machine_dynamics, \
_human_logs, _machine_logs = self.data[100]
frame = self.renderer.render(_human_orientations,
_machine_orientations, _human_dynamics,
_machine_dynamics, _human_logs,
_machine_logs)
self.assertEqual(self.renderer.mode, 'drgb')
self.assertEqual(frame.shape, (378, 378, 3))
def test_close(self):
# Initialize a pyglet renderer
self.renderer = Renderer(self.network,
mode=self.mode,
save_render=self.save_render,
sight_radius=self.sight_radius,
pxpm=self.pxpm,
show_radius=self.show_radius,
alpha=self.alpha)
self.renderer.close()
_human_orientations, _machine_orientations, \
_human_dynamics, _machine_dynamics, \
_human_logs, _machine_logs = self.data[1]
self.assertRaises(ctypes.ArgumentError, self.renderer.render,
_human_orientations, _machine_orientations,
_human_dynamics, _machine_dynamics, _human_logs,
_machine_logs)
def test_get_sight(self):
# Initialize a pyglet renderer
self.renderer = Renderer(self.network,
mode=self.mode,
save_render=self.save_render,
sight_radius=self.sight_radius,
pxpm=self.pxpm,
show_radius=self.show_radius,
alpha=self.alpha)
_human_orientations, _machine_orientations, \
_human_dynamics, _machine_dynamics, \
_human_logs, _machine_logs = self.data[101]
self.renderer.render(_human_orientations, _machine_orientations,
_human_dynamics, _machine_dynamics, _human_logs,
_machine_logs)
orientation = self.data[101][0][0]
id = self.data[101][4][0][-1]
sight = self.renderer.get_sight(orientation, id)
self.assertEqual(sight.shape, (150, 150, 3))
def test_save_renderer(self):
self.save_render = True
# Initialize a pyglet renderer
self.renderer = Renderer(self.network,
mode=self.mode,
save_render=self.save_render,
path='/tmp',
sight_radius=self.sight_radius,
pxpm=self.pxpm,
show_radius=self.show_radius,
alpha=self.alpha)
_human_orientations, _machine_orientations, \
_human_dynamics, _machine_dynamics, \
_human_logs, _machine_logs = self.data[101]
self.renderer.render(_human_orientations, _machine_orientations,
_human_dynamics, _machine_dynamics, _human_logs,
_machine_logs)
save_path = self.renderer.close()
saved_data = np.load(save_path, allow_pickle=True)
self.assertEqual(self.data[0], saved_data[0])
self.assertEqual(self.data[101], saved_data[1])
def __init__(self, env_params, sim_params, scenario, simulator='traci'):
"""Initialize the environment class.
Parameters
----------
env_params : flow.core.params.EnvParams
see flow/core/params.py
sim_params : flow.core.params.SimParams
see flow/core/params.py
scenario : flow.scenarios.Scenario
see flow/scenarios/base_scenario.py
simulator : str
the simulator used, one of {'traci', 'aimsun'}. Defaults to 'traci'
Raises
------
flow.utils.exceptions.FatalFlowError
if the render mode is not set to a valid value
"""
# Invoke serializable if using rllab
if serializable_flag:
Serializable.quick_init(self, locals())
self.env_params = env_params
self.scenario = scenario
self.net_params = scenario.net_params
self.initial_config = scenario.initial_config
self.sim_params = sim_params
time_stamp = ''.join(str(time.time()).split('.'))
if os.environ.get("TEST_FLAG", 0):
# 1.0 works with stress_test_start 10k times
time.sleep(1.0 * int(time_stamp[-6:]) / 1e6)
# FIXME: this is sumo-specific
self.sim_params.port = sumolib.miscutils.getFreeSocketPort()
# time_counter: number of steps taken since the start of a rollout
self.time_counter = 0
# step_counter: number of total steps taken
self.step_counter = 0
# initial_state:
self.initial_state = {}
self.state = None
self.obs_var_labels = []
# simulation step size
self.sim_step = sim_params.sim_step
# the simulator used by this environment
self.simulator = simulator
# create the Flow kernel
self.k = Kernel(simulator=self.simulator, sim_params=sim_params)
# use the scenario class's network parameters to generate the necessary
# scenario components within the scenario kernel
self.k.scenario.generate_network(scenario)
# initial the vehicles kernel using the VehicleParams object
self.k.vehicle.initialize(deepcopy(scenario.vehicles))
# initialize the simulation using the simulation kernel. This will use
# the scenario kernel as an input in order to determine what network
# needs to be simulated.
kernel_api = self.k.simulation.start_simulation(
scenario=self.k.scenario, sim_params=sim_params)
# pass the kernel api to the kernel and it's subclasses
self.k.pass_api(kernel_api)
# the available_routes variable contains a dictionary of routes
# vehicles can traverse; to be used when routes need to be chosen
# dynamically
self.available_routes = self.k.scenario.rts
# store the initial vehicle ids
self.initial_ids = deepcopy(scenario.vehicles.ids)
# store the initial state of the vehicles kernel (needed for restarting
# the simulation)
self.k.vehicle.kernel_api = None
self.k.vehicle.master_kernel = None
self.initial_vehicles = deepcopy(self.k.vehicle)
self.k.vehicle.kernel_api = self.k.kernel_api
self.k.vehicle.master_kernel = self.k
self.setup_initial_state()
# use pyglet to render the simulation
if self.sim_params.render in ['gray', 'dgray', 'rgb', 'drgb']:
save_render = self.sim_params.save_render
sight_radius = self.sim_params.sight_radius
pxpm = self.sim_params.pxpm
show_radius = self.sim_params.show_radius
# get network polygons
network = []
# FIXME: add to scenario kernel instead of hack
for lane_id in self.k.kernel_api.lane.getIDList():
_lane_poly = self.k.kernel_api.lane.getShape(lane_id)
lane_poly = [i for pt in _lane_poly for i in pt]
network.append(lane_poly)
# instantiate a pyglet renderer
self.renderer = Renderer(network,
self.sim_params.render,
save_render,
sight_radius=sight_radius,
pxpm=pxpm,
show_radius=show_radius)
# render a frame
self.render(reset=True)
elif self.sim_params.render in [True, False]:
pass # default to sumo-gui (if True) or sumo (if False)
else:
raise FatalFlowError('Mode %s is not supported!' %
self.sim_params.render)
atexit.register(self.terminate)
def __init__(self, env_params, sim_params, scenario):
# Invoke serializable if using rllab
if serializable_flag:
Serializable.quick_init(self, locals())
self.env_params = env_params
self.scenario = scenario
self.sim_params = sim_params
time_stamp = ''.join(str(time.time()).split('.'))
if os.environ.get("TEST_FLAG", 0):
# 1.0 works with stress_test_start 10k times
time.sleep(1.0 * int(time_stamp[-6:]) / 1e6)
# FIXME: this is sumo-specific
self.sim_params.port = sumolib.miscutils.getFreeSocketPort()
self.vehicles = scenario.vehicles
self.traffic_lights = scenario.traffic_lights
# time_counter: number of steps taken since the start of a rollout
self.time_counter = 0
# step_counter: number of total steps taken
self.step_counter = 0
# initial_state:
# Key = Vehicle ID,
# Entry = (type_id, route_id, lane_index, lane_pos, speed, pos)
self.initial_state = {}
self.state = None
self.obs_var_labels = []
# simulation step size
self.sim_step = sim_params.sim_step
# TraCI connection used to communicate with sumo
self.traci_connection = None
# dictionary of initial observations used while resetting vehicles
# after each rollout
self.initial_observations = dict.fromkeys(self.vehicles.get_ids())
# store the initial vehicle ids
self.initial_ids = deepcopy(self.vehicles.get_ids())
# store the initial state of the vehicles class (for restarting sumo)
self.initial_vehicles = deepcopy(self.vehicles)
# colors used to distinguish between types of vehicles in the network
self.colors = {}
# the simulator used by this environment
self.simulator = 'traci'
# create the Flow kernel
self.k = Kernel(simulator=self.simulator,
sim_params=sim_params)
# use the scenario class's network parameters to generate the necessary
# scenario components within the scenario kernel
self.k.scenario.generate_network(scenario)
# initialize the simulation using the simulation kernel. This will use
# the scenario kernel as an input in order to determine what network
# needs to be simulated.
self.traci_connection = self.k.simulation.start_simulation(
scenario=self.k.scenario, sim_params=sim_params)
# pass the kernel api to the kernel and it's subclasses
self.k.pass_api(self.traci_connection)
# the available_routes variable contains a dictionary of routes
# vehicles can traverse; to be used when routes need to be chosen
# dynamically
self.available_routes = self.k.scenario.rts
self.setup_initial_state()
# use pyglet to render the simulation
if self.sim_params.render in ['gray', 'dgray', 'rgb', 'drgb']:
save_render = self.sim_params.save_render
sight_radius = self.sim_params.sight_radius
pxpm = self.sim_params.pxpm
show_radius = self.sim_params.show_radius
# get network polygons
network = []
for lane_id in self.traci_connection.lane.getIDList():
_lane_poly = self.traci_connection.lane.getShape(lane_id)
lane_poly = [i for pt in _lane_poly for i in pt]
network.append(lane_poly)
# instantiate a pyglet renderer
self.renderer = Renderer(
network,
self.sim_params.render,
save_render,
sight_radius=sight_radius,
pxpm=pxpm,
show_radius=show_radius)
# render a frame
self.render(reset=True)
elif self.sim_params.render in [True, False]:
pass # default to sumo-gui (if True) or sumo (if False)
else:
raise ValueError("Mode %s is not supported!" %
self.sim_params.render)
atexit.register(self.terminate)
def __init__(self,
env_params,
sim_params,
network=None,
simulator='traci',
scenario=None):
"""Initialize the environment class.
Parameters
----------
env_params : flow.core.params.EnvParams
see flow/core/params.py
sim_params : flow.core.params.SimParams
see flow/core/params.py
network : flow.networks.Network
see flow/networks/base.py
simulator : str
the simulator used, one of {'traci', 'aimsun'}. Defaults to 'traci'
Raises
------
flow.utils.exceptions.FatalFlowError
if the render mode is not set to a valid valuefor _ in range(self.env_params.sims_per_step):
self.time_counter += 1
self.step_counter += 1
# perform acceleration actions for controlled human-driven vehicles
if len(self.k.vehicle.get_controlled_ids()) > 0:
accel = []
for veh_id in self.k.vehicle.get_controlled_ids():
action = self.k.vehicle.get_acc_controller(
veh_id).get_action(self)
accel.append(action)
if self.k.vehicle.get_edge(veh_id)[0] == ":":
if self.k.vehicle.get_speed(veh_id) <= 0.00000001:
print(self.time_counter,'veh_id:',veh_id,'its leader:',self.k.vehicle.get_leader(veh_id),'headway to leader:',self.k.vehicle.get_headway(veh_id),'action:',action,'speed:',self.k.vehicle.get_speed(veh_id))
#if self.k.vehicle.get_leader(self.k.vehicle.get_leader(veh_id))==veh_id:
# break
self.k.vehicle.apply_acceleration(
self.k.vehicle.get_controlled_ids(), accel)
# perform lane change actions for controlled human-driven vehicles
if len(self.k.vehicle.get_controlled_lc_ids()) > 0:
direction = []
for veh_id in self.k.vehicle.get_controlled_lc_ids():
target_lane = self.k.vehicle.get_lane_changing_controller(
veh_id).get_action(self)
direction.append(target_lane)
self.k.vehicle.apply_lane_change(
self.k.vehicle.get_controlled_lc_ids(),
direction=direction)
# perform (optionally) routing actions for all vehicles in the
# network, including RL and SUMO-controlled vehicles
routing_ids = []
routing_actions = []
for veh_id in self.k.vehicle.get_ids():
if self.k.vehicle.get_routing_controller(veh_id) \
is not None:
routing_ids.append(veh_id)
route_contr = self.k.vehicle.get_routing_controller(
veh_id)
routing_actions.append(route_contr.choose_route(self))
self.k.vehicle.choose_routes(routing_ids, routing_actions)
self.apply_rl_actions(rl_actions)
#self.additional_command()
# advance the simulation in the simulator by one step
"""
self.process_seeds_file = None
self.env_params = env_params
self.time_with_no_vehicles = 0
if scenario is not None:
deprecated_attribute(self, "scenario", "network")
self.network = scenario if scenario is not None else network
self.net_params = self.network.net_params
self.initial_config = self.network.initial_config
self.sim_params = sim_params
time_stamp = ''.join(str(time.time()).split('.'))
if os.environ.get("TEST_FLAG", 0):
# 1.0 works with stress_test_start 10k times
time.sleep(1.0 * int(time_stamp[-6:]) / 1e6)
# FIXME: this is sumo-specific
self.sim_params.port = sumolib.miscutils.getFreeSocketPort()
# time_counter: number of steps taken since the start of a rollout
self.time_counter = 0
# step_counter: number of total steps taken
self.step_counter = 0
# initial_state:
self.initial_state = {}
self.state = None
self.obs_var_labels = []
# simulation step size
self.sim_step = sim_params.sim_step
# the simulator used by this environment
self.simulator = simulator
# create the Flow kernel
self.k = Kernel(simulator=self.simulator, sim_params=sim_params)
# use the network class's network parameters to generate the necessary
# network components within the network kernel
self.k.network.generate_network(self.network)
# initial the vehicles kernel using the VehicleParams object
self.k.vehicle.initialize(deepcopy(self.network.vehicles))
# initialize the simulation using the simulation kernel. This will use
# the network kernel as an input in order to determine what network
# needs to be simulated.
kernel_api = self.k.simulation.start_simulation(network=self.k.network,
sim_params=sim_params)
# pass the kernel api to the kernel and it's subclasses
self.k.pass_api(kernel_api)
# the available_routes variable contains a dictionary of routes
# vehicles can traverse; to be used when routes need to be chosen
# dynamically
self.available_routes = self.k.network.rts
# store the initial vehicle ids
self.initial_ids = deepcopy(self.network.vehicles.ids)
# store the initial state of the vehicles kernel (needed for restarting
# the simulation)
self.k.vehicle.kernel_api = None
self.k.vehicle.master_kernel = None
self.initial_vehicles = deepcopy(self.k.vehicle)
self.k.vehicle.kernel_api = self.k.kernel_api
self.k.vehicle.master_kernel = self.k
self.setup_initial_state()
# use pyglet to render the simulation
if self.sim_params.render in ['gray', 'dgray', 'rgb', 'drgb']:
save_render = self.sim_params.save_render
sight_radius = self.sim_params.sight_radius
pxpm = self.sim_params.pxpm
show_radius = self.sim_params.show_radius
# get network polygons
network = []
# FIXME: add to network kernel instead of hack
for lane_id in self.k.kernel_api.lane.getIDList():
_lane_poly = self.k.kernel_api.lane.getShape(lane_id)
lane_poly = [i for pt in _lane_poly for i in pt]
network.append(lane_poly)
# instantiate a pyglet renderer
self.renderer = Renderer(network,
self.sim_params.render,
save_render,
sight_radius=sight_radius,
pxpm=pxpm,
show_radius=show_radius)
# render a frame
self.render(reset=True)
elif self.sim_params.render in [True, False]:
pass # default to sumo-gui (if True) or sumo (if False)
else:
raise FatalFlowError('Mode %s is not supported!' %
self.sim_params.render)
atexit.register(self.terminate)
def __init__(self, env_params, sumo_params, scenario):
# Invoke serializable if using rllab
if Serializable is not object:
Serializable.quick_init(self, locals())
self.env_params = env_params
self.scenario = scenario
self.sumo_params = sumo_params
time_stamp = ''.join(str(time.time()).split('.'))
if os.environ.get("TEST_FLAG", 0):
# 1.0 works with stress_test_start 10k times
time.sleep(1.0 * int(time_stamp[-6:]) / 1e6)
self.sumo_params.port = sumolib.miscutils.getFreeSocketPort()
self.vehicles = scenario.vehicles
self.traffic_lights = scenario.traffic_lights
# time_counter: number of steps taken since the start of a rollout
self.time_counter = 0
# step_counter: number of total steps taken
self.step_counter = 0
# initial_state:
# Key = Vehicle ID,
# Entry = (type_id, route_id, lane_index, lane_pos, speed, pos)
self.initial_state = {}
self.state = None
self.obs_var_labels = []
# simulation step size
self.sim_step = sumo_params.sim_step
self.vehicle_arrangement_shuffle = \
env_params.vehicle_arrangement_shuffle
self.starting_position_shuffle = env_params.starting_position_shuffle
# the available_routes variable contains a dictionary of routes
# vehicles can traverse; to be used when routes need to be chosen
# dynamically
self.available_routes = self.scenario.rts
# TraCI connection used to communicate with sumo
self.traci_connection = None
# dictionary of initial observations used while resetting vehicles
# after each rollout
self.initial_observations = dict.fromkeys(self.vehicles.get_ids())
# store the initial vehicle ids
self.initial_ids = deepcopy(self.vehicles.get_ids())
# store the initial state of the vehicles class (for restarting sumo)
self.initial_vehicles = deepcopy(self.vehicles)
# colors used to distinguish between types of vehicles in the network
self.colors = {}
# contains the subprocess.Popen instance used to start traci
self.sumo_proc = None
self.start_sumo()
self.setup_initial_state()
# use pyglet to render the simulation
if self.sumo_params.render in ['gray', 'dgray', 'rgb', 'drgb']:
save_render = self.sumo_params.save_render
sight_radius = self.sumo_params.sight_radius
pxpm = self.sumo_params.pxpm
show_radius = self.sumo_params.show_radius
# get network polygons
network = []
for lane_id in self.traci_connection.lane.getIDList():
_lane_poly = self.traci_connection.lane.getShape(lane_id)
lane_poly = [i for pt in _lane_poly for i in pt]
network.append(lane_poly)
# instantiate a pyglet renderer
self.renderer = Renderer(network,
self.sumo_params.render,
save_render,
sight_radius=sight_radius,
pxpm=pxpm,
show_radius=show_radius)
# render a frame
self.render(reset=True)
elif self.sumo_params.render in [True, False]:
pass # default to sumo-gui (if True) or sumo (if False)
else:
raise ValueError("Mode %s is not supported!" %
self.sumo_params.render)
def test_pyglet_renderer(self):
# Ring road network polygons
network = \
[[36.64, -1.6500000000000001, 38.15, -1.62, 39.69, -1.52,
41.22, -1.37, 42.74, -1.1500000000000001, 44.26, -0.88, 45.77,
-0.53, 47.25, -0.13, 48.72, 0.32, 50.17, 0.84, 51.61, 1.41,
53.01, 2.05, 54.39, 2.74, 55.730000000000004, 3.48,
57.050000000000004, 4.2700000000000005, 58.34, 5.12, 59.59, 6.03,
60.800000000000004, 6.98, 61.97, 7.97, 63.11, 9.02, 64.2, 10.11,
65.25, 11.25, 66.24, 12.42, 67.19, 13.63, 68.1, 14.88, 68.95,
16.17, 69.74, 17.490000000000002, 70.48, 18.830000000000002,
71.17, 20.21, 71.81, 21.61, 72.38, 23.05, 72.9, 24.5,
73.35000000000001, 25.97, 73.75, 27.45, 74.10000000000001,
28.96, 74.37, 30.48, 74.59, 32.0, 74.74, 33.53, 74.84, 35.07,
74.87, 36.58],
[-1.6500000000000001, 36.58, -1.62, 35.07, -1.52, 33.53, -1.37,
32.0, -1.1500000000000001, 30.48, -0.88, 28.96, -0.53, 27.45,
-0.13, 25.97, 0.32, 24.5, 0.84, 23.05, 1.41, 21.61, 2.05, 20.21,
2.74, 18.830000000000002, 3.48, 17.490000000000002,
4.2700000000000005, 16.17, 5.12, 14.88, 6.03, 13.63, 6.98, 12.42,
7.97, 11.25, 9.02, 10.11, 10.11, 9.02, 11.25, 7.97, 12.42, 6.98,
13.63, 6.03, 14.88, 5.12, 16.17, 4.2700000000000005,
17.490000000000002, 3.48, 18.830000000000002, 2.74, 20.21, 2.05,
21.61, 1.41, 23.05, 0.84, 24.5, 0.32, 25.97, -0.13, 27.45,
-0.53, 28.96, -0.88, 30.48, -1.1500000000000001, 32.0, -1.37,
33.53, -1.52, 35.07, -1.62, 36.58, -1.6500000000000001],
[74.87, 36.64, 74.84, 38.15, 74.74, 39.69, 74.59, 41.22, 74.37,
42.74, 74.10000000000001, 44.26, 73.75, 45.77, 73.35000000000001,
47.25, 72.9, 48.72, 72.38, 50.17, 71.81, 51.61, 71.17, 53.01,
70.48, 54.39, 69.74, 55.730000000000004, 68.95,
57.050000000000004, 68.1, 58.34, 67.19, 59.59, 66.24,
60.800000000000004, 65.25, 61.97, 64.2, 63.11, 63.11, 64.2,
61.97, 65.25, 60.800000000000004, 66.24, 59.59, 67.19, 58.34,
68.1, 57.050000000000004, 68.95, 55.730000000000004, 69.74,
54.39, 70.48, 53.01, 71.17, 51.61, 71.81, 50.17, 72.38, 48.72,
72.9, 47.25, 73.35000000000001, 45.77, 73.75, 44.26,
74.10000000000001, 42.74, 74.37, 41.22, 74.59, 39.69, 74.74,
38.15, 74.84, 36.64, 74.87],
[36.58, 74.87, 35.07, 74.84, 33.53, 74.74, 32.0, 74.59, 30.48,
74.37, 28.96, 74.10000000000001, 27.45, 73.75, 25.97,
73.35000000000001, 24.5, 72.9, 23.05, 72.38, 21.61, 71.81, 20.21,
71.17, 18.830000000000002, 70.48, 17.490000000000002, 69.74,
16.17, 68.95, 14.88, 68.1, 13.63, 67.19, 12.42, 66.24, 11.25,
65.25, 10.11, 64.2, 9.02, 63.11, 7.97, 61.97, 6.98,
60.800000000000004, 6.03, 59.59, 5.12, 58.34, 4.2700000000000005,
57.050000000000004, 3.48, 55.730000000000004, 2.74, 54.39, 2.05,
53.01, 1.41, 51.61, 0.84, 50.17, 0.32, 48.72, -0.13, 47.25,
-0.53, 45.77, -0.88, 44.26, -1.1500000000000001, 42.74, -1.37,
41.22, -1.52, 39.69, -1.62, 38.15, -1.6500000000000001, 36.64]]
# Renderer parameters
mode = "drgb"
save_render = False
sight_radius = 25
pxpm = 3
show_radius = True
# initialize a pyglet renderer
renderer = Renderer(network,
mode,
save_render=save_render,
sight_radius=sight_radius,
pxpm=pxpm,
show_radius=show_radius)
# ensure that the attributes match their correct values
self.assertEqual(renderer.mode, mode)
self.assertEqual(renderer.save_render, save_render)
self.assertEqual(renderer.sight_radius, sight_radius)
self.assertEqual(renderer.pxpm, pxpm)
self.assertEqual(renderer.show_radius, show_radius)
