Autonomous driving using deep reinforcement learning Algorithms on Carla Simulator 2

2020-2021 tavasz

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Téma leírása

An important part of the Autonomous Driving System (ADS) is the steering system which supposed
to emulate the behavior of human drivers as a self-driving car controller. This eliminates the need
for human engineers to anticipate what is important in an image and to foresee all the necessary
rules for safe driving. The most mature machine learning framework that can be put forward to do
such task is Deep Reinforcement Learning (DRL) due to its ability to work and interact with virtual
simulation environment.
In thesis thesis work, a DRL model is designed based on asynchronous advantage actor-critic (A3C)
algorithm. The asynchronous nature of the method enables running multiple simulation threads in
parallel which is important given the high sample complexity of deep reinforcement learning. Also,
we try to build our model using Deep Deterministic Policy Gradients (DDPG) algorithm to solve
the problem of continuous action space. Finally, we try to compare between the two algorithms to
see which is better in continuous action control. The objective of the Steering Model is to perform
complex tasks such as; lane keeping, lane changing and overtaking.

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