FURI | Spring 2018
Reinforcement Learning with Randomized Rewards
Current reinforcement learning models are brittle and unable to accept change in reward structure. In order to obtain a true general AI our machine learning algorithms must be able to expect changes in a reward structure. That is why the research is taking a look at randomizing the reward that is obtained in simple machine learning experiments. One of the areas that have been developed by CUbic reasarches is the implementation of infrastructure and monitoring software. To solve this problem, CUbIC has developed custom software using python to show training time and hardware statistics.
Hometown: Tempe, Arizona
Graduation date: Spring 2022