John Schulman

I'm a research scientist at OpenAI, and was previously a PhD student at UC Berkeley Pieter Abbeel's group. I study deep reinforcement learning, i.e., reinforcement learning using nonlinear function approximators (such as neural networks), which are optimized by gradient-based algorithms. I strive to develop policy optimization methods that are robust, scalable, and sample-efficient. This research is inspired by my earlier work in robotics, where I mainly investigated the following two problems: (1) teaching robots to perform manipulation tasks using human demonstrations, work that enabled autonomous knot tying and surgical suturing; (2) using trajectory optimization for motion planning. The software library developed for this project has been used on a variety of real robots, including one scary humanoid.


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