Deep Q-Managed: a new framework for multi-objective deep reinforcement learning

This paper introduces Deep Q-Managed, a novel multi-objective reinforcement leaning (MORL) algorithm designed to discover all policies within the Pareto Front. This approach enhances multi-objective optimization by integrating deep leaning techniques, including Double and Dueling Networks, to effectively mitigate the curve of dimensionality and overestimation bias. Deep Q-Managed demonstrates high proficiency in attaining non-dominated multi-objective policies across deterministic episodic environments, adapting to convex, concave, or mixed Pareto Front complexities. Experiments on traditional MORL benchmarks (Deep Sea Treasure, Bountiful Sea Treasure, and Modified Bountiful Sea Treasure) show it consistently achieves maximum hypervolume values (e.g., 1,155 for DST, 3,352 for BST, and 2,632 for MBST) and locates all Pareto Front points. While robust and versatile for practical applications in robotics, finance, and healthcare, this study's validation is currently confined to deterministic episodic settings, with stochastic environments reserved for future work.