Richard Liaw

Real-Time Machine Learning: The Missing Pieces

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.

SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards

We present sequential windowed inverse reinforcement learning (SWIRL), a policy search algorithm that is a hybrid of exploration and demonstration paradigms for robot learning. We apply unsupervised learning to a small number of initial expert demonstrations to structure future autonomous exploration. SWIRL approximates a long time horizon task as a sequence of local reward functions and subtask transition conditions. Over this approximation, SWIRL applies Q-learning to compute a policy that maximizes rewards. Experiments suggest that SWIRL requires significantly fewer rollouts than pure reinforcement learning and fewer expert demonstrations than behavioral cloning to learn a policy. We evaluate SWIRL in two simulated control tasks, parallel parking and a two-link pendulum. On the parallel parking task, SWIRL achieves the maximum reward on the task with 85% fewer rollouts than Q-learning, and one-eight of demonstrations needed by behavioral cloning. We also consider physical experiments on surgical tensioning and cutting deformable sheets using a da Vinci surgical robot. On the deformable tensioning task, SWIRL achieves a 36% relative improvement in reward compared with a baseline of behavioral cloning with segmentation.