Description
Chair: Kyunghyun Cho
Reinforcement Learning (RL) has had many recent successes in games and
other applications where accurate, cheap simulations are available or a
large amount of learning trials are possible. In doing so, it has
demonstrated its ability to learn control and decision making policies for
large-scale, nonlinear, hidden state systems that are too challenging for
scientists to manually design policies. The problem of designing control
policies for nuclear fusion in tokamaks contains all these features of a
difficult dynamic system and the challenge of not having good simulators or
the ability to run many experiments.
In this talk, I will describe our recent efforts at overcoming these
challenges on the DIII-D tokamak. I will first present our algorithms for
learning dynamic models with uncertainty quantification and then the
corresponding RL and Bayesian optimization algorithms that build on those
models to produce control policies. Finally, I will summarize the results
of testing these policies on the DIII-D tokamak over the past 6 months.
