TLDR; If you’re writing a textbook, add summary sections illustrating clearly what is and isn’t essential to memorize, indicate which exercises are useful and in what way they’re useful, and attempt to add a narrative.
My name is Eugene Vinitsky; I’m a graduate student in Controls at UC Berkeley under Prof. Alexandre Bayen. My primary interest is in using reinforcement learning to design controllers for complex systems. In particular, I’ve focused on using reinforcement learning to help autonomous vehicles mitigate poor human driving patterns. Besides this, I’m interested in multi-agent RL, condensed matter physics, and developments in ML.
PhD in Controls in progress
M.A. in Physics, 2015
UC Santa Barbara
BSc in Physics, 2014
Flow is a pythonic simulator/control environment for assessing the effects of vehicle/traffic light control strategies on traffic. It interfaces SUMO (a traffic simulator) with RLlib/RLlab (reinforcement learning libraries) to enable researchers to train controllers for improving traffic objectives such as average speed or throughput.
We examine the motions of particles in quadrupole ion traps as a function of damping and trapping forces, including cases where nonlinear damping or nonlinearities in the electric field geometry play significant roles. We report the discovery of a new collective behavior in damped 2D microparticle ion traps, where particles spontaneously assemble into a remarkable knot of overlapping, corotating diamond orbits, self-stabilized by air currents arising from the particle motion.
We present a study of the high-pressure behavior of BaReH9, a novel hydrogen-rich compound, using optical, Raman, and infrared spectroscopy as well as synchrotron x-ray diffraction. The x-ray diffraction measurements demonstrate that BaReH9 retains its hexagonal structure on room temperature compression up to 40 GPa. Optical absorption shows the absence of a gap closure to 80 GPa. Raman and IR spectra reveal the pressure evolution of a newly observed phonon peak, and large peak broadening with increasing pressure. These data constrain the disorder present in the material following the P-T paths explored.