Reinforcement learning (RL) - a branch of machine learning - refers to the process of an agent learning to achieve a certain goal by interaction with its environment. The process of conventional tunneling shows many similarities, where a geotechnician (agent) tries to achieve a breakthrough (goal) by excavating the rockmass (environment) in an optimum way.

In this paper we present a novel RL based framework for strategy development for conventional tunneling. We developed a virtual environment with the goal of a tunnel breakthrough and with a deep Q-network as the agent's architecture. It can choose from different excavation sequences to reach that goal and learns to do so in an economical and safe way by getting feedback from a specially designed reward system. Result analyses show that the optimal policies have great similarities to current practices of sequential tunneling and the framework has the potential to discover new tunneling strategies.

强化学习（RL）是机器学习的一个分支，它是指代理学习通过与环境交互来实现某个目标的过程。传统的掘进过程显示出许多相似之处，其中，岩土工程师（代理商）试图通过以最佳方式挖掘岩体（环境）来实现突破（目标）。

在本文中，我们提出了一种新颖的基于RL的框架，用于常规隧道的策略开发。我们开发了一个虚拟环境，旨在突破隧道并以深层Q网络作为代理的体系结构。它可以从不同的挖掘顺序中进行选择，以达到该目标，并通过从专门设计的奖励系统获得反馈来学习以经济，安全的方式做到这一点。结果分析表明，最优策略与当前的顺序隧道实践有很大的相似性，并且该框架具有发现新隧道策略的潜力。