Computer Engineering Commons^™

A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire

Honors Theses

Reinforcement learning is thought to be a promising branch of machine learning that has the potential to help us develop an Artificial General Intelligence (AGI) machine. Among the machine learning algorithms, primarily, supervised, semi supervised, unsupervised and reinforcement learning, reinforcement learning is different in a sense that it explores the environment without prior knowledge, and determines the optimal action. This study attempts to understand the concept behind reinforcement learning, the mathematics behind it and see it in action by deploying the trained model in Amazon's DeepRacer car. DeepRacer, a 1/18^th scaled autonomous car, is the agent which is trained …

Development Of A Fully Instrumented, Resonant Tensegrity Strut, Kentaro Barhydt

Honors Theses

A tensegrity is a structure composed of a series of rigid members connected in static equilibrium by tensile elements. A vibrating tensegrity robot is an underactuated system in which a set of its struts are vibrated at certain frequency combinations to achieve various locomotive gaits. Evolutionary robotics research lead by Professor John Rieffel focuses on exploiting the complex dynamics of tensegrity structures to control locomotion in vibrating tensegrity robots by finding desired gaits using genetic algorithms. A current hypothesis of interest is that the optimal locomotive gaits of a vibrating tensegrity exist at its resonant frequencies.

In order to observe …