Proximal Policy Optimization for lane following
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2020-07-10
Language
en
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Abstract
This thesis aimed to apply a state of the art reinforcement learning algorithm
named proximal policy optimization on a complicated task with real world
applicability in which sensor data is not always reliable. This algorithm was
tested on the task of lane following. In order to do this the autonomous
car simulator Carla was used. Semantic segmentation and Canny lter were
discussed as methods to extract the lanes from the RGB sensor that the
Carla simulator provided. The agent's performance was then examined on
one of Carla's maps. In the end it turned out to be impossible to run
the experiment due through hardware limitations. As an alternative, the
algorithm was tested on the Luna lander environment, a game in which the
agent had to land a rocket on the moon. Adding Gaussian noise to the
agent's sensors did not prevent the algorithm from converging. It could be
concluded from this that proximal policy optimization can derive an optimal
policy on easy environments even if the sensor data is not completely reliable.
There are, however, limits to the amount of noise that can be added.
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Faculteit der Sociale Wetenschappen