07-28, 20:10–20:20 (UTC), Green
POMDPs.jl is a leading research tool for partially observable Markov decision processes that also enables new teaching opportunities. This talk will describe POMDPs.jl and the Decision Making under Uncertainty class at CU Boulder. Each assignment in this class includes an open-ended challenge problem where students implement algorithms in Julia that are auto-graded. The system enables challenging assignments such as programming MCTS with a 100ms time limit and DQN for reinforcement learning.
The course materials website, including notes and homework assignments, is located here: https://github.com/zsunberg/CU-DMU-Materials, and the Julia package for the course is located here: https://github.com/zsunberg/DMUStudent.jl. The algorithms that the students implement in Julia include Value Iteration, Monte Carlo Tree Search, DQN, and QMDP. The algorithms are graded on the students' machine to ease debugging. This talk will give a very-brief overview of POMDPs.jl, and discuss the course, what went well, and what aspects turned out to be challenging.
(this talk could be expanded into a 30 minute talk if there is enough interest).
Assistant Professor of Aerospace Engineering at the University of Colorado