Extended Radial Basis Function Controller for Reinforcement Learning

There have been attempts in model-based reinforcement learning to exploit a priori knowledge about the structure of the system. This paper introduces the extended radial basis function (RBF) controller design. In addition to traditional RBF controllers, our controller comprises of an engineered linear controller inside an operating region. We show that the learnt extended RBF controller takes on the desirable characteristics of both the linear and non-linear controller models. The extended controller is shown to retain the ability for universal function approximation of the non-linear RBF functions. At the same time, it demonstrates desirable stability criteria on par with the linear controller. Learning has been done in a probabilistic inference framework (PILCO), but could generalise to other reinforcement learning frameworks. Experimental results from the Swing-up pendulum, Cartpole, and Mountain car environments are reported.

Improving LIME Robustness with Smarter Locality Sampling

Explainability algorithms such as LIME have enabled machine learning systems to adopt transparency and fairness, which are important qualities in commercial use cases. However, recent work has shown that LIME's naive sampling strategy can be exploited by an adversary to conceal biased, harmful behavior. We propose to make LIME more robust by training a generative adversarial network to sample more realistic synthetic data which the explainer uses to generate explanations. Our experiments demonstrate that our proposed method demonstrates an increase in accuracy across three real-world datasets in detecting biased, adversarial behavior compared to vanilla LIME. This is achieved while maintaining comparable explanation quality, with up to 99.94\% in top-1 accuracy in some cases.