  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.
