Reinforcement learning is a paradigm in machine learning that uses incentives- or “reinforcement”- to drive learning. The learner is conceptualized as an intelligent agent working within a system of rewards and penalties in order to solve a novel problem. The agent is designed to maximize rewards while pursuing a solution by trial-and-error. 

Programming a system to respond to the complex and unpredictable “real world” is one of the principal challenges in robotics engineering. One field which is finding new applications for reinforcement learning is the study of MEMS devices- robots or other electronic devices built at the micrometer scale. The use of reinforcement learning in microscopic devices poses a challenging engineering problem, due to constraints with power usage and computational power.

Nathan Lambert is a PhD student at Berkeley who works with the Berkeley Autonomous Microsystems Lab. He has also worked at Facebook AI Research and Tesla. He joins the show today to talk about the application of reinforcement learning to robotics and how deep learning is changing the MEMS device landscape.

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