Task-based dialogue policy learning based on diffusion models

The purpose of task-based dialogue systems is to help users achieve their dialogue needs using as few dialogue rounds as possible. As the demand increases, the dialogue tasks gradually involve multiple domains and develop in the direction of complexity and diversity. Achieving high performance with low computational effort has become an essential metric for multi-domain task-based dialogue systems. This paper proposes a new approach to guided dialogue policy. The method introduces a conditional diffusion model in the reinforcement learning Q-learning algorithm to regularise the policy in a diffusion Q-learning manner. The conditional diffusion model is used to learn the action value function, regulate the actions using regularisation, sample the actions, use the sampled actions in the policy update process, and additionally add a loss term that maximizes the value of the actions in the policy update process to improve the learning efficiency. Our proposed method is based on a conditional diffusion model, combined with the reinforcement learning TD3 algorithm as a dialogue policy and an inverse reinforcement learning approach to construct a reward estimator to provide rewards for policy updates as a way of completing a multi-domain dialogue task.