Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/74098
Title: Reinforcement learning for self-driving cars
Authors: Ho, Song Yan
Keywords: DRNTU::Engineering
Issue Date: 2018
Abstract: This project presents the implementation of deep learning model to act as a self-driving car- agent to maximize its speed on a multilane expressway. This project includes the development of traffic environment simulation, the design of neural network model, and the implementation of reinforcement learning algorithm. The proposed model uses the minimal sensory input collected from the environment. The model was trained with reinforcement learning algorithm in the simulation environment to simulate traffic condition of seven-lane expressway. The model successfully learns and applies the optimal policy. The model was tested under three different traffic conditions to determine its performance statistically. The best model is the model with neural network configuration that approximate to the optimal Q-learning function. The source code of this project can be found on https://github.com/songyanho/Reinforcement- Learning-for-Self-Driving-Cars.
URI: http://hdl.handle.net/10356/74098
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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