96 lines
3.1 KiB
Markdown
96 lines
3.1 KiB
Markdown
|
Customizing the Agent Benchmark
|
||
|
|
---------------------------
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
The agent benchmark is associated with other two modules.
|
||
|
|
The *agent* module, that is a controller which performs in a
|
||
|
|
second module, the *experiment suite*.
|
||
|
|
Both modules are abstract classes that must be redefined by
|
||
|
|
the user.
|
||
|
|
|
||
|
|
In the following code excerpt is
|
||
|
|
a example on how to apply a agent benchmark
|
||
|
|
|
||
|
|
agent = Forward()
|
||
|
|
experiment_suite = Basic()
|
||
|
|
benchmark = AgentBenchmark()
|
||
|
|
benchmark_summary = benchmark.benchmark_agent(experiment_suite, agent, client)
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
In this tutorial we are going to show how to create
|
||
|
|
a basic experimental suite and a trivial forward going agent.
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
####Defining the Agent
|
||
|
|
|
||
|
|
The tested agent must inherit the base *Agent* class
|
||
|
|
|
||
|
|
from carla.agent.agent import Agent
|
||
|
|
from carla.client import VehicleControl
|
||
|
|
|
||
|
|
class Forward(Agent):
|
||
|
|
|
||
|
|
|
||
|
|
To have its performance evaluated, the Forward derived class must redefine the *run_step*
|
||
|
|
function as it is done in the following excerpt:
|
||
|
|
|
||
|
|
def run_step(self, measurements, sensor_data, directions, target):
|
||
|
|
"""
|
||
|
|
Function to run a control step in the CARLA vehicle.
|
||
|
|
"""
|
||
|
|
control = VehicleControl()
|
||
|
|
control.throttle = 0.9
|
||
|
|
return control
|
||
|
|
|
||
|
|
|
||
|
|
The function receives [measurements](measurements.md) from the world,
|
||
|
|
sensor data and a target position. With this,
|
||
|
|
the function must return a control to the car,
|
||
|
|
*i.e.* steering value, throttle value, brake value, etc.
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
####Defining the Experiment Suite
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
Benchmarking your Agent
|
||
|
|
---------------------
|
||
|
|
|
||
|
|
corl = CoRL2017(city_name=args.city_name, name_to_save=args.log_name)
|
||
|
|
agent = Manual(args.city_name)
|
||
|
|
results = corl.benchmark_agent(agent, client)
|
||
|
|
|
||
|
|
This excerpt is executed at the
|
||
|
|
[run_benchmark.py](https://github.com/carla-simulator/carla/blob/master/PythonClient/run_benchmark.py) example.
|
||
|
|
|
||
|
|
First a *benchmark* object is defined, for this case, a CoRL2017 benchmark.
|
||
|
|
This is object is used to benchmark a certain Agent. <br>
|
||
|
|
On the second line of our sample code, there is an object of a Manual class instanced.
|
||
|
|
This class inherited an Agent base class
|
||
|
|
that is used by the *benchmark* object.
|
||
|
|
To be benchmarked, an Agent subclass must redefine the *run_step*
|
||
|
|
function as it is done in the following excerpt:
|
||
|
|
|
||
|
|
def run_step(self, measurements, sensor_data, directions, target):
|
||
|
|
"""
|
||
|
|
Function to run a control step in the CARLA vehicle.
|
||
|
|
:param measurements: object of the Measurements type
|
||
|
|
:param sensor_data: images list object
|
||
|
|
:param target: target position of Transform type
|
||
|
|
:return: an object of the control type.
|
||
|
|
"""
|
||
|
|
control = VehicleControl()
|
||
|
|
control.throttle = 0.9
|
||
|
|
return control
|
||
|
|
The function receives measurements from the world, sensor data and a target position. With this,
|
||
|
|
the function must return a control to the car, *i.e.* steering value, throttle value, brake value, etc.
|
||
|
|
|
||
|
|
The [measurements](measurements.md), [target](measurements.md), [sensor_data](cameras_and_sensors.md)
|
||
|
|
and [control](measurements.md) types are described on the documentation.
|