Towards Goal-Oriented Agents for Evolving Problems Observed via
  Conversation
Michael Free Andrew Langworthy Mary Dimitropoulaki Simon Thompson
The objective of this work is to train a chatbot capable of solving evolving
problems through conversing with a user about a problem the chatbot cannot
directly observe. The system consists of a virtual problem (in this case a
simple game), a simulated user capable of answering natural language questions
that can observe and perform actions on the problem, and a Deep Q-Network
(DQN)-based chatbot architecture. The chatbot is trained with the goal of
solving the problem through dialogue with the simulated user using
reinforcement learning. The contributions of this paper are as follows: a
proposed architecture to apply a conversational DQN-based agent to evolving
problems, an exploration of training methods such as curriculum learning on
model performance and the effect of modified reward functions in the case of
increasing environment complexity.