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.

翻译：本工作旨在训练一个能够通过与用户就智能体无法直接观察的问题进行对话来解决演化问题的聊天机器人。系统由虚拟问题（本文中为简单游戏）、能够回答自然语言问题且可观察并执行问题操作的模拟用户，以及基于深度Q网络（DQN）的聊天机器人架构组成。该聊天机器人通过强化学习训练，目标是与模拟用户对话以解决问题。本文的贡献如下：提出了一种将基于对话的DQN智能体应用于演化问题的架构；探索了课程学习等训练方法对模型性能的影响；以及研究了在环境复杂度递增情况下修改奖励函数所产生的效果。