def test_bottleneck(self):
"""Verifies that examples/sumo/bottlenecks.py is working."""
# import the experiment variable from the example
exp = bottleneck_example(20, 5, render=False)
# run the experiment for a few time steps to ensure it doesn't fail
exp.run(1, 5)
def run_bottleneck(flow_rate, num_trials, num_steps, render=None):
print('Running experiment for inflow rate: ', flow_rate, render)
exp = bottleneck_example(flow_rate, num_steps, restart_instance=True)
info_dict = exp.run(num_trials, num_steps)
return info_dict['average_outflow'], \
np.mean(info_dict['velocities']), \
np.mean(info_dict['average_rollout_density_outflow']), \
info_dict['per_rollout_outflows'], \
flow_rate
def run_bottleneck(flow_rate,
penetration_rate,
num_trials,
num_steps,
render=None,
disable_ramp_meter=True,
n_crit=8,
feedback_coef=20,
lc_on=False,
q_init=400):
"""Run a rollout of the bottleneck environment.
Parameters
----------
flow_rate : float
bottleneck inflow rate
num_trials : int
number of rollouts to perform
num_steps : int
number of simulation steps per rollout
render : bool
whether to render the environment
Returns
-------
float
average outflow rate across rollouts
float
average speed across rollouts
float
average rollout density outflow
list of float
per rollout outflows
float
inflow rate
"""
print('Running experiment for inflow rate: ', flow_rate, render, q_init,
penetration_rate, feedback_coef)
exp = bottleneck_example(flow_rate,
num_steps,
render=render,
restart_instance=True,
disable_ramp_meter=disable_ramp_meter,
feedback_coef=feedback_coef,
n_crit=n_crit,
lc_on=lc_on,
q_init=q_init,
penetration_rate=penetration_rate)
info_dict = exp.run(num_trials, num_steps)
return info_dict['average_outflow'], \
info_dict['velocities'], \
np.mean(info_dict['average_rollout_density_outflow']), \
info_dict['per_rollout_outflows'], \
flow_rate, info_dict['lane_4_vels'],
def run_bottleneck(flow_rate, num_trials, num_steps, render=None):
"""Run a rollout of the bottleneck environment.
Parameters
----------
flow_rate : float
bottleneck inflow rate
num_trials : int
number of rollouts to perform
num_steps : int
number of simulation steps per rollout
render : bool
whether to render the environment
Returns
-------
float
average outflow rate across rollouts
float
average speed across rollouts
float
average rollout density outflow
list of float
per rollout outflows
float
inflow rate
"""
print('Running experiment for inflow rate: ', flow_rate, render)
exp = bottleneck_example(flow_rate, num_steps, restart_instance=True)
info_dict = exp.run(num_trials, num_steps)
return info_dict['average_outflow'], \
np.mean(info_dict['velocities']), \
np.mean(info_dict['average_rollout_density_outflow']), \
info_dict['per_rollout_outflows'], \
flow_rate
