Deep Reinforcement Learning for Conversational AI

TL;DR

This paper reviews how deep reinforcement learning enhances conversational AI by discussing key concepts, challenges, and models, highlighting its potential to improve autonomous conversational systems.

Contribution

It provides a comprehensive overview of deep reinforcement learning techniques and their application to conversational AI, including challenges and model discussions.

Findings

Identifies key challenges in applying reinforcement learning to conversational AI.

Discusses various deep reinforcement learning-based conversational models.

Highlights the importance of reward functions and model design in system performance.

Abstract

Deep reinforcement learning is revolutionizing the artificial intelligence field. Currently, it serves as a good starting point for constructing intelligent autonomous systems which offer a better knowledge of the visual world. It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels in playing video games. In this paper, key concepts of deep reinforcement learning including reward function, differences between reinforcement learning and supervised learning and models for implementation of reinforcement are discussed. Key challenges related to the implementation of reinforcement learning in conversational AI domain are identified as well as discussed in detail. Various conversational models which are based on deep reinforcement learning (as well as deep learning) are also discussed. In summary, this paper discusses key aspects of deep reinforcement learning which are crucial for designing an efficient conversational AI.