def _build_configuration(self):
"""Builds a configuration using an SAC agent
"""
# TODO(@hart): increase scenario number
self._scenario_generator = \
ConfigurableScenarioGeneration(num_scenarios=100,
params=self._params)
# USE THIS FOR DETERMINISTIC SCENARIO GEN.
# self._scenario_generator = \
# DeterministicScenarioGeneration(num_scenarios=100,
# params=self._params)
# self._observer = NearestObserver(self._params)
self._observer = ClosestAgentsObserver(self._params)
self._behavior_model = DynamicModel(params=self._params)
self._evaluator = CustomEvaluator(params=self._params)
sim_step_time = 0.2
real_time_factor = 5
resolution = (1920, 1080)
dpi = 300
fig_env = plt.figure(figsize=(resolution[0] / dpi,
resolution[1] / dpi),
dpi=dpi)
gs = gridspec.GridSpec(1, 1, left=0.0, right=1, bottom=0, top=0.9)
axis = plt.subplot(gs[0])
viewer = MPViewer(params=self._params,
y_length=80,
enforce_y_length=True,
enforce_x_length=False,
follow_agent_id=True,
axis=axis)
# self._viewer = VideoRenderer(renderer=viewer, world_step_time=0.2)
self._viewer = viewer
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))
eval_tf_env = tf_py_environment.TFPyEnvironment(
TFAWrapper(self._runtime))
# self._agent = PPOAgent(tfa_env, params=self._params)
# self._runner = PPORunner(tfa_env,
# eval_tf_env,
# self._agent,
# params=self._params,
# unwrapped_runtime=self._runtime)
self._agent = SACAgent(tfa_env, params=self._params)
self._runner = SACRunner(tfa_env,
self._agent,
params=self._params,
unwrapped_runtime=self._runtime)
def test_configurable_scenario_generation_default_params(self):
params = ParameterServer()
scenario_generation = ConfigurableScenarioGeneration(num_scenarios=2,params=params)
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))
scenario = scenario_loader.get_scenario(idx=0)
params.save("default_params.json")
def test_configurable_scenario_generation_sample_behavior_types(self):
sink_source_dict = [{
"SourceSink": [[5111.626, 5006.8305], [5110.789, 5193.1725]],
"Description":
"left_lane",
"ConfigAgentStatesGeometries": {
"Type": "UniformVehicleDistribution",
"LanePositions": [0]
},
"ConfigBehaviorModels": {
"Type": "FixedBehaviorType",
"ModelType": "BehaviorIDMClassic",
"ModelParams": {
"BehaviorIDMClassic::MaxVelocity": 60.0
}
},
"ConfigExecutionModels": {
"Type": "FixedExecutionType"
},
"ConfigDynamicModels": {
"Type": "FixedDynamicType"
},
"ConfigGoalDefinitions": {
"Type": "FixedGoalTypes"
},
"ConfigControlledAgents": {
"Type": "NoneControlled"
},
"AgentParams": {}
}, {
"SourceSink": [[5111.626, 5006.8305], [5110.789, 5193.1725]],
"Description":
"right_lane",
"ConfigAgentStatesGeometries": {
"Type": "UniformVehicleDistribution",
"LanePositions": [1]
},
"ConfigBehaviorModels": {
"Type": "SampleBehaviorType"
},
"ConfigExecutionModels": {
"Type": "FixedExecutionType"
},
"ConfigDynamicModels": {
"Type": "FixedDynamicType"
},
"ConfigGoalDefinitions": {
"Type": "FixedGoalTypes"
},
"ConfigControlledAgents": {
"Type": "RandomSingleAgent"
},
"AgentParams": {}
}]
params = ParameterServer()
params["Scenario"]["Generation"]["ConfigurableScenarioGeneration"][
"SinksSources"] = sink_source_dict
scenario_generation = ConfigurableScenarioGeneration(num_scenarios=2,
params=params)
scenario_generation.dump_scenario_list("test.scenario")
params.save("default_params_behavior_type_sampling.json")
# https://opensource.org/licenses/MIT
from modules.runtime.scenario.scenario_generation.configurable_scenario_generation import ConfigurableScenarioGeneration
from modules.runtime.commons.parameters import ParameterServer
from modules.runtime.viewer.matplotlib_viewer import MPViewer
from modules.runtime.runtime import Runtime
from modules.runtime.viewer.pygame_viewer import PygameViewer
import time
import os
scenario_param_file ="highway_merge_configurable.json" # must be within examples params folder
param_server = ParameterServer(filename= os.path.join("examples/params/",scenario_param_file))
scenario_generation = ConfigurableScenarioGeneration(num_scenarios=3, 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]
env = Runtime(0.2,
viewer,