Deep Reinforcement Learning for Conversational AI

2017/09/15 by Mahipal Jadeja, Jadeja, Mahipal, Neelanshi Varia +3
Computer Science · #AI in Service Interactions #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1709.05067

openalex publication_date 2017/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Attention with Intention for a Neural Network Conversation Model
• Strategic Dialogue Management via Deep Reinforcement Learning
• A Persona-Based Neural Conversation Model

Related

• Deep Reinforcement Learning: An Overview
• Deep Reinforcement Learning
• Deep Reinforcement Learning
• Deep Reinforcement Learning for Dialogue Generation
• Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces