Training a deep reinforcement learning-based dialogue policy with brute-force random
sampling is costly. A new training paradigm was proposed to improve learning performance …

A dialogue policy module is an essential part of task-completion dialogue systems. Recently,
increasing interest has focused on reinforcement learning (RL)-based dialogue policy. Its …

Formulating dialogue policy as a reinforcement learning (RL) task enables a dialogue
system to act optimally by interacting with humans. However, typical RL-based methods …

Deep learning, especially deep reinforcement learning (DRL), has become one of the most
useful tools for solving sequential decision-making problems. This is particularly relevant to …

Training a task-oriented dialogue policy using deep reinforcement learning is promising but
requires extensive environment exploration. The amount of wasted invalid exploration …

Training a satisfactory dialogue policy via Reinforcement Learning (RL) requires significant
interaction costs because of delayed and sparse rewards in task-oriented dialogue tasks …

Many methods have been proposed to use reinforcement learning to train dialogue policy
for task-oriented dialogue systems in recent years. However, the high cost of interacting with …

We live in the era of ubiquitous sensing and computing. More and more data is being
collected and processed from devices, sensors and systems. This opens up opportunities to …

Deep Reinforcement learning (DRL) has been successfully applied to the dialogue policy of
task-oriented dialogue systems. However, one challenge in the existing DRL-based …

Les systèmes de dialogue orientés tâche, plus communément appelés chatbots, ont pour
but de réaliser des tâches et de fournir des informations à la demande d'un utilisateur dans …