Alternating Optimisation and Quadrature for Robust Control

Authors

  • Supratik Paul University of Oxford
  • Konstantinos Chatzilygeroudis Inria, Villers-lès-Nancy
  • Kamil Ciosek University of Oxford
  • Jean-Baptiste Mouret Inria, Villers-lès-Nancy
  • Michael Osborne University of Oxford
  • Shimon Whiteson University of Oxford

DOI:

https://doi.org/10.1609/aaai.v32i1.11687

Keywords:

Reinforcement Learning, Bayesian Optimisation, Bayesian Quadrature

Abstract

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator. This paper considers the problem of finding a robust policy while taking into account the impact of environment variables. We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings. ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but play a substantial role in determining the optimal policy. Experimental results across different domains show that ALOQ can learn more efficiently and robustly than existing methods.

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Published

2018-04-29

How to Cite

Paul, S., Chatzilygeroudis, K., Ciosek, K., Mouret, J.-B., Osborne, M., & Whiteson, S. (2018). Alternating Optimisation and Quadrature for Robust Control. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11687