Because reinforcement learning suffers from a lack of scalability, online value (and Q-) function approximation has received increasing interest this last decade. This contribution introduces a novel approximation scheme, namely the Kalman Temporal Differences (KTD) framework, that exhibits the following features: sample-efficiency, non-linear approximation, non-stationarity handling and uncertainty management. A first KTD-based algorithm is provided for deterministic Markov Decision Processes (MDP) which produces biased estimates in the case of stochastic transitions. Than the eXtended KTD framework (XKTD), solving stochastic MDP, is described. Convergence is analyzed for special cases for both deterministic and stochastic transitions. Related algorithms are experimented on classical benchmarks. They compare favorably to the state of the art while exhibiting the announced features

Abstract-The design of Spoken Dialog Systems cannot be considered as the simple combination of speech processing technologies. Indeed, speech-based interface design has been an expert job for a long time. It necessitates good skills in speech technologies and low-level programming. Moreover, rapid development and reusability of previously designed systems remains uneasy. This makes optimality and objective evaluation of design very difficult. The design process is therefore a cyclic process composed of prototype releases, user satisfaction surveys, bug reports and refinements. It is well known that human intervention for testing is time-consuming and above all very expensive. This is one of the reasons for the recent interest in dialog simulation for evaluation as well as for design automation and optimization. In this paper we expose a probabilistic framework for a realistic simulation of spoken dialogs in which the major components of a dialog system are modeled and parameterized thanks to independent data or expert knowledge. Especially, an Automatic Speech Recognition (ASR) system model and a User Model (UM) have been developed. The ASR model, based on articulatory similarities in language models, provides task-adaptive performance prediction and Confidence Level (CL) distribution estimation. The user model relies on the Bayesian Networks (BN) paradigm and is used both for user behavior modeling and Natural Language Understanding (NLU) modeling. The complete simulation framework has been used to train a reinforcement-learning agent on two different tasks. These experiments helped to point out several potentially problematic dialog scenarios.

International audienceUser simulation is an important research area in the field of spoken dialogue systems (SDSs) because collecting and annotating real human-machine interactions is often expensive and time-consuming. However, such data are generally required for designing, training and assessing dialogue systems. User simulations are especially needed when using machine learning methods for optimizing dialogue management strategies such as Reinforcement Learning, where the amount of data necessary for training is larger than existing corpora. The quality of the user simulation is therefore of crucial importance because it dramatically influences the results in terms of SDS performance analysis and the learnt strategy. Assessment of the quality of simulated dialogues and user simulation methods is an open issue and, although assessment metrics are required, there is no commonly adopted metric. In this paper, we give a survey of User Simulations Metrics in the literature, propose some extensions and discuss these metrics in terms of a list of desired features

Reinforcement learning (RL) is a machine learning answer to the optimal control problem. It consists of learning an optimal control policy through interactions with the system to be controlled, the quality of this policy being quantified by the so-called value function. A recurrent subtopic of RL concerns computing an approximation of this value function when the system is too large for an exact representation. This survey reviews state-of-the-art methods for (parametric) value function approximation by grouping them into three main categories: bootstrapping, residual, and projected fixed-point approaches. Related algorithms are derived by considering one of the associated cost functions and a specific minimization method, generally a stochastic gradient descent or a recursive least-squares approach.