Title: A Multi-Objective Deep Reinforcement Learning Framework,
Abstract: This paper presents a new multi-objective deep reinforcement learning (MODRL)
framework based on deep Q-networks. We propose the use of linear and non-linear
methods to develop the MODRL framework that includes both single-policy and
multi-policy strategies. The experimental results on two benchmark problems
including the two-objective deep sea treasure environment and the
three-objective mountain car problem indicate that the proposed framework is
able to converge to the optimal Pareto solutions effectively. The proposed
framework is generic, which allows implementation of different deep
reinforcement learning algorithms in different complex environments. This
therefore overcomes many difficulties involved with standard multi-objective
reinforcement learning (MORL) methods existing in the current literature. The
framework creates a platform as a testbed environment to develop methods for
solving various problems associated with the current MORL. Details of the
framework implementation can be referred to
this http URL.