This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark problems (two-objective deep sea treasure environment and three-objective Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal solutions effectively. The proposed framework is generic and highly modularized, which allows the integration of different deep reinforcement learning algorithms in different complex problem domains. This therefore overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the current literature. The proposed framework acts as a testbed platform that accelerates the development of MODRL for solving increasingly complicated multi-objective problems.

本文介绍了一种基于深度Q网络的可扩展的多目标深度强化学习（MODRL）新框架。我们开发了一种高性能的MODRL框架，该框架同时支持单策略和多策略策略，以及线性和非线性方法来选择动作。对两个基准问题（两目标深海宝藏环境和三目标山车问题）的实验结果表明，所提出的框架能够有效地找到帕累托最优解。所提出的框架是通用的且高度模块化的，从而允许在不同的复杂问题域中集成不同的深度强化学习算法。因此，这克服了当前文献中与标准多目标强化学习方法有关的许多缺点。