Rowan McAllister
now a Staff Research Scientist at Waymo
Publications
Bayesian Learning for Data-Efficient Control
Rowan McAllister, 2016. University of Cambridge, Department of Engineering, Cambridge, UK.
Abstract▼ URL
Applications to learn control of unfamiliar dynamical systems with increasing autonomy are ubiquitous. From robotics, to finance, to industrial processing, autonomous learning helps obviate a heavy reliance on experts for system identification and controller design. Often real world systems are nonlinear, stochastic, and expensive to operate (e.g. slow, energy intensive, prone to wear and tear). Ideally therefore, nonlinear systems can be identified with minimal system interaction. This thesis considers data efficient autonomous learning of control of nonlinear, stochastic systems. Data efficient learning critically requires probabilistic modelling of dynamics. Traditional control approaches use deterministic models, which easily overfit data, especially small datasets. We use probabilistic Bayesian modelling to learn systems from scratch, similar to the PILCO algorithm, which achieved unprecedented data efficiency in learning control of several benchmarks. We extend PILCO in three principle ways. First, we learn control under significant observation noise by simulating a filtered control process using a tractably analytic framework of Gaussian distributions. In addition, we develop the `latent variable belief Markov decision process’ when filters must predict under real-time constraints. Second, we improve PILCO’s data efficiency by directing exploration with predictive loss uncertainty and Bayesian optimisation, including a novel approximation to the Gittins index. Third, we take a step towards data efficient learning of high-dimensional control using Bayesian neural networks (BNN). Experimentally we show although filtering mitigates adverse effects of observation noise, much greater performance is achieved when optimising controllers with evaluations faithful to reality: by simulating closed-loop filtered control if executing closed-loop filtered control. Thus, controllers are optimised w.r.t. how they are used, outperforming filters applied to systems optimised by unfiltered simulations. We show directed exploration improves data efficiency. Lastly, we show BNN dynamics models are almost as data efficient as Gaussian process models. Results show data efficient learning of high-dimensional control is possible as BNNs scale to high-dimensional state inputs.
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning,
Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller, August 2017. (In International Joint Conference on Artificial Intelligence). Melbourne, Australia.
Abstract▼ URL
Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AV software must consider the ultimate effect of each component’s errors. Further, improving safety alone is not sufficient. Passengers must also feel safe to trust and use AV systems. To address such concerns, we investigate three under-explored themes for AV research: safety, interpretability, and compliance. Safety can be improved by quantifying the uncertainties of component outputs and propagating them forward through the pipeline. Interpretability is concerned with explaining what the AV observes and why it makes the decisions it does, building reassurance with the passenger. Compliance refers to maintaining some control for the passenger. We discuss open challenges for research within these themes. We highlight the need for concrete evaluation metrics, propose example problems, and highlight possible solutions.
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
Rowan McAllister, Carl Edward Rasmussen, December 2017. (In Advances in Neural Information Processing Systems 31). Long Beach, California.
Abstract▼ URL
We present a data-efficient reinforcement learning method for continuous state-action systems under significant observation noise. Data-efficient solutions under small noise exist, such as PILCO which learns the cartpole swing-up task in 30s. PILCO evaluates policies by planning state-trajectories using a dynamics model. However, PILCO applies policies to the observed state, therefore planning in observation space. We extend PILCO with filtering to instead plan in belief space, consistent with partially observable Markov decisions process (POMDP) planning. This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original (unfiltered) PILCO algorithm. We test our method on the cartpole swing-up task, which involves nonlinear dynamics and requires nonlinear control.