Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sample-efficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actor-critic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learn deep RL-based dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.
Combining SL with RL for dialogue modelling is not new. proposed a hybrid SL RL model that, in order to ensure tractability in policy optimisation, performed exploration only on the states in a dialogue corpus. The policy was then defined manually on parts of the space which were not found in the corpus. A method of initialising RL models using logistic regression was also described @cite_46 . For GPRL in dialogue, rather than using a linear kernel that imposes heuristic data pair correlation, a pre-optimised Gaussian kernel learned using SL from a dialogue corpus has been proposed @cite_32 . The resulting kernel was more accurate on data correlation and achieved better performance, however, the SL corpus did not help to initialise a better policy. Better initialisation of GPRL has been studied in the context of domain adaptation by specifying a GP prior or re-using an existing model which is then pre-trained for the new domain @cite_16 .
@cite_46 @cite_16 @cite_32
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We investigate the use of logistic regression (LR) to initialise Reinforcement Learning (RL)-based dialogue systems with models of human dialogue strategies. LR produces accurate predictions and performs feature selection. We illustrate this technique in exploring human multimodal clarification strategies, observed in a Wizard-of-Oz experiment. We use it to initialise an RL-based system with features which significantly influence human behaviour. We show that the strategy applied by the human wizards is sensitive to different dialogue contexts. Furthermore we show that for predicting clarification behaviour the logistic models improve over the baseline on average twice as much as the supervised learning techniques used in previous work. Gaussian processes reinforcement learning provides an appealing framework for training the dialogue policy as it takes into account correlations of the objective function given different dialogue belief states, which can significantly speed up the learning. These correlations are modelled by the kernel function which may depend on hyper-parameters. So far, for real-world dialogue systems the hyperparameters have been hand-tuned, relying on the designer to adjust the correlations, or simple non-parametrised kernel functions have been used instead. Here, we examine different kernel structures and show that it is possible to optimise the hyperparameters from data yielding improved performance of the resulting dialogue policy. We confirm this in a real user trial.