Beyond Deep Learning: Synthesizing Navigation Programs using Neural Turing Machines
Description
This thesis aims to improve neural control policies for self-driving cars. State-of-the-art navigation software for self-driving cars is based on deep neural networks, where the network is trained on a dataset of past driving experience in various situations. With previous methods, the car can only make decisions based on short-term memory. To address this problem, we proposed that using a Neural Turing Machine (NTM) framework adds long-term memory to the system. We evaluated this approach by using it to master a palindrome task. The network was able to infer how to create a palindrome with 100% accuracy. Since the NTM structure proves useful, we aim to use it in the given scenarios to improve the navigation safety and accuracy of a simulated autonomous car.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2018-05
Agent
- Author (aut): Martin, Sarah
- Thesis director: Ben Amor, Hani
- Committee member: Fainekos, Georgios
- Contributor (ctb): Barrett, The Honors College