def test_uniform_vehicle_distribution_default_params(self):
scenario_generation = UniformVehicleDistribution(num_scenarios=2, random_seed=0)
scenario_generation.dump_scenario_list("test.scenario")
scenario_loader = ScenarioGeneration()
scenario_loader.load_scenario_list("test.scenario")
self.assertEqual(len(scenario_loader._scenario_list), 2)
self.assertEqual(len(scenario_loader._scenario_list[0]._agent_list), len(scenario_generation._scenario_list[0]._agent_list))
def _build_configuration(self):
"""Builds a configuration using an SAC agent
"""
self._scenario_generator = \
UniformVehicleDistribution(num_scenarios=20,
random_seed=0,
params=self._params)
self._observer = CustomObserver(params=self._params)
self._behavior_model = DynamicModel(params=self._params)
self._evaluator = CustomEvaluator(params=self._params)
self._viewer = MPViewer(params=self._params,
x_range=[-30, 30],
y_range=[-60, 20],
follow_agent_id=True)
#self._viewer = VideoRenderer(renderer=viewer, world_step_time=0.2)
self._runtime = RuntimeRL(action_wrapper=self._behavior_model,
observer=self._observer,
evaluator=self._evaluator,
step_time=0.2,
viewer=self._viewer,
scenario_generator=self._scenario_generator)
tfa_env = tf_py_environment.TFPyEnvironment(TFAWrapper(self._runtime))
self._agent_0 = SACAgent(tfa_env, params=self._params)
self._agent_1 = SACAgent(tfa_env, params=self._params)
self._runner = SACRunner(tfa_env, [self._agent_0, self._agent_1],
params=self._params,
unwrapped_runtime=self._runtime)
def test_motion_primitives_concat_state(self):
params = ParameterServer(
filename="modules/runtime/tests/data/highway_merging.json")
scenario_generation = UniformVehicleDistribution(num_scenarios=3,
random_seed=0,
params=params)
state_observer = StateConcatenation()
action_wrapper = MotionPrimitives()
evaluator = GoalReached()
viewer = PygameViewer(
params=params, use_world_bounds=True
) # x_range=[-40,40], y_range=[-40,40], follow_agent_id=True)
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
nn_observer=state_observer,
evaluator=evaluator,
step_time=0.2,
viewer=viewer,
scenario_generator=scenario_generation)
for _ in range(0, 5): # run 5 scenarios in a row, repeating after 3
nn_state = runtimerl.reset()
for _ in range(0, 10): # run each scenario for 10 steps
next_nn_state, reward, done, info = runtimerl.step(
action_wrapper.action_space.sample())
runtimerl.render()
print("State: {} \n Reward: {} \n Done {}, Info: {} \n \
=================================================".
format(next_nn_state, reward, done, info))
def test_motion_primitives_concat_state():
params = ParameterServer(
filename="modules/runtime/tests/data/highway_merging.json")
scenario_generation = UniformVehicleDistribution(num_scenarios=3,
random_seed=0,
params=params)
state_observer = StateConcatenation(params=params)
action_wrapper = MotionPrimitives(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-20, 40],
follow_agent_id=True) #use_world_bounds=True) #
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
nn_observer=state_observer,
evaluator=evaluator,
step_time=0.05,
viewer=viewer,
scenario_generator=scenario_generation)
tfa_env = TFAWrapper(runtimerl)
_ = tfa_env.reset()
utils.validate_py_environment(tfa_env, episodes=5)
_ = tf_py_environment.TFPyEnvironment(tfa_env)
def test_motion_primitives_concat_state(self):
params = ParameterServer(
filename="modules/runtime/tests/data/highway_merging.json")
scenario_generation = UniformVehicleDistribution(num_scenarios=3,
random_seed=0,
params=params)
state_observer = StateConcatenation(params=params)
action_wrapper = MotionPrimitives(params=params)
evaluator = GoalReached(params=params)
viewer = MPViewer(params=params,
x_range=[-30, 30],
y_range=[-20, 40],
follow_agent_id=True) #use_world_bounds=True) #
runtimerl = RuntimeRL(action_wrapper=action_wrapper,
nn_observer=state_observer,
evaluator=evaluator,
step_time=0.05,
viewer=viewer,
scenario_generator=scenario_generation)
for _ in range(0, 5): # run 5 scenarios in a row, repeating after 3
nn_state = runtimerl.reset()
i = 0
actionsteps = []
for _ in range(0, 1000): # run each scenario for 1000 steps
actionstep = action_wrapper.action_space.sample()
next_nn_state, reward, done, info = runtimerl.step(actionstep)
actionsteps.append(actionstep)
i += 1
runtimerl.render()
if info["success"] or done:
print("Finish in ", i)
print("Actions taken:", actionsteps)
print("State: {} \n Reward: {} \n Done {}, Info: {} \n \
=================================================".
format(next_nn_state, reward, done, info))
break
params.save(filename="highway_merging_written.json")
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
from modules.runtime.scenario.scenario_generation.uniform_vehicle_distribution import UniformVehicleDistribution
from modules.runtime.commons.parameters import ParameterServer
from modules.runtime.viewer.matplotlib_viewer import MPViewer
from modules.runtime.viewer.pygame_viewer import PygameViewer
import time
import os
scenario_param_file = "highway_merging.json" # must be within examples params folder
param_server = ParameterServer(
filename=os.path.join("examples/params/", scenario_param_file))
scenario_generation = UniformVehicleDistribution(num_scenarios=3,
random_seed=0,
params=param_server)
viewer = MPViewer(params=param_server,
x_range=[5060, 5160],
y_range=[5070, 5150],
use_world_bounds=True)
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation", 0.2]
sim_real_time_factor = param_server["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
# TODO(@hart): does not work with bazel test //... because of read only file-system
# scenario_generation.dump_scenario_list(filename="examples/scenarios/highway_merging_dump.bark_scenarios")
# load scenario list
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
# ffmpeg must be installed and available on command line
from modules.runtime.scenario.scenario_generation.uniform_vehicle_distribution import UniformVehicleDistribution
from modules.runtime.commons.parameters import ParameterServer
from modules.runtime.viewer.matplotlib_viewer import MPViewer
from modules.runtime.viewer.video_renderer import VideoRenderer
import os
scenario_param_file ="highway_merging.json" # must be within examples params folder
param_server = ParameterServer(filename= os.path.join("examples/params/",scenario_param_file))
scenario_generation = UniformVehicleDistribution(num_scenarios=10, random_seed=0, params=param_server)
viewer = MPViewer(params=param_server, x_range=[5060, 5160], y_range=[5070,5150])
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation",
0.2]
sim_real_time_factor = param_server["simulation"]["real_time_factor",
"execution in real-time or faster", 1]
scenario, idx = scenario_generation.get_next_scenario()
# Rendering WITHOUT intermediate steps
video_renderer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time)
for _ in range(0, 1):
scenario, idx = scenario_generation.get_next_scenario()
# https://opensource.org/licenses/MIT
from modules.runtime.scenario.scenario_generation.uniform_vehicle_distribution import UniformVehicleDistribution
from modules.runtime.commons.parameters import ParameterServer
from modules.runtime.viewer.pygame_viewer import PygameViewer
import time
import os
scenario_dump_folder = "examples/scenarios/"
scenario_param_file = "highway_merging.json" # must be within scenario dump folder
param_server = ParameterServer(
filename=os.path.join("examples/params/", scenario_param_file))
scenario_generation = UniformVehicleDistribution(num_scenarios=3,
random_seed=0,
params=param_server)
scenario_generation.params.save(
os.path.join(scenario_dump_folder, scenario_param_file))
viewer = PygameViewer(params=param_server, use_world_bounds=True)
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation",
0.05]
sim_real_time_factor = param_server["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
for _ in range(0, 2): # run 5 scenarios in a row, repeating after 3
scenario, idx = scenario_generation.get_next_scenario()
world_state = scenario.get_world_state()
print("Running scenario {} of {}".format(
# Copyright (c) 2019 fortiss GmbH
#
# This software is released under the MIT License.
# https://opensource.org/licenses/MIT
from modules.runtime.scenario.scenario_generation.uniform_vehicle_distribution import UniformVehicleDistribution
from modules.runtime.commons.parameters import ParameterServer
import time
import os
param_server = ParameterServer()
scenario_generation = UniformVehicleDistribution(num_scenarios=3,
random_seed=0,
params=param_server)
scenario_generation.dump_scenario_list("test.bark_scenarios")