Stochastic Inverse Reinforcement Learning

The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many reward functions, and expert demonstrations may be optimal for many policies. In this work, we generalize the IRL problem to a well-posed expectation optimization problem stochastic inverse reinforcement learning (SIRL) to recover the probability distribution over reward functions. We adopt the Monte Carlo expectation-maximization (MCEM) method to estimate the parameter of the probability distribution as the first solution to the SIRL problem. The solution is succinct, robust, and transferable for a learning task and can generate alternative solutions to the IRL problem. Through our formulation, it is possible to observe the intrinsic property for the IRL problem from a global viewpoint, and our approach achieves a considerable performance on the objectworld.Comment: 8+2 pages, 5 figures, Under Revie

Evaluations of infinite series involving reciprocal hyperbolic functions

This paper presents a approach of summation of infinite series of hyperbolic functions. The approach is based on simple contour integral representions and residue computations with the help of some well known results of Eisenstein series given by Ramanujan and Berndt et al. Several series involving quadratic hyperbolic functions are evaluated, which can be expressed in terms of $z={}_2F_1(1/2,1/2;1;x)$ and $z'=dz/dx$. When a certain parameter in these series equal to $\pi$ the series are summable in terms of $\Gamma$ functions. Moreover, some interesting new consequences and illustrative examples are considered

Entity Recognition at First Sight: Improving NER with Eye Movement Information

Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models. In this work, we leverage eye movement features from three corpora with recorded gaze information to augment a state-of-the-art neural model for named entity recognition (NER) with gaze embeddings. These corpora were manually annotated with named entity labels. Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time. The gaze-augmented models for NER using token-level and type-level features outperform the baselines. We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.Comment: Accepted at NAACL-HLT 201