def merge_baseline(num_runs, flow_params, render=True):
"""Run script for all merge baselines.
Parameters
----------
num_runs : int
number of rollouts the performance of the environment is evaluated
over
flow_params : dict
the flow meta-parameters describing the structure of a benchmark.
Must be one of the merge flow_params
render: bool, optional
specifies whether to use sumo's gui during execution
Returns
-------
SumoExperiment
class needed to run simulations
"""
exp_tag = flow_params['exp_tag']
sumo_params = flow_params['sumo']
vehicles = flow_params['veh']
env_params = flow_params['env']
net_params = flow_params['net']
initial_config = flow_params.get('initial', InitialConfig())
traffic_lights = flow_params.get('tls', TrafficLights())
# modify the rendering to match what is requested
sumo_params.render = render
# set the evaluation flag to True
env_params.evaluate = True
# import the scenario class
module = __import__('flow.scenarios', fromlist=[flow_params['scenario']])
scenario_class = getattr(module, flow_params['scenario'])
# create the scenario object
scenario = scenario_class(name=exp_tag,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=traffic_lights)
# import the environment class
module = __import__('flow.envs', fromlist=[flow_params['env_name']])
env_class = getattr(module, flow_params['env_name'])
# create the environment object
env = env_class(env_params, sumo_params, scenario)
exp = SumoExperiment(env, scenario)
results = exp.run(num_runs, env_params.horizon)
avg_speed = np.mean(results['mean_returns'])
return avg_speed
def merge_baseline(num_runs, render=True):
"""Run script for all merge baselines.
Parameters
----------
num_runs : int
number of rollouts the performance of the environment is evaluated
over
render: bool, optional
specifies whether to use the gui during execution
Returns
-------
flow.core.experiment.Experiment
class needed to run simulations
"""
exp_tag = flow_params['exp_tag']
sim_params = flow_params['sim']
vehicles = flow_params['veh']
env_params = flow_params['env']
net_params = flow_params['net']
initial_config = flow_params.get('initial', InitialConfig())
traffic_lights = flow_params.get('tls', TrafficLightParams())
# modify the rendering to match what is requested
sim_params.render = render
# set the evaluation flag to True
env_params.evaluate = True
# import the network class
module = __import__('flow.networks', fromlist=[flow_params['network']])
network_class = getattr(module, flow_params['network'])
# create the network object
network = network_class(name=exp_tag,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=traffic_lights)
# import the environment class
module = __import__('flow.envs', fromlist=[flow_params['env_name']])
env_class = getattr(module, flow_params['env_name'])
# create the environment object
env = env_class(env_params, sim_params, network)
exp = Experiment(env)
results = exp.run(num_runs, env_params.horizon)
avg_speed = np.mean(results['mean_returns'])
return avg_speed