An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments

no code implementations • 6 Jan 2024 • Kewen Ding, Peter Vamplew, Cameron Foale, Richard Dazeley

One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function.

Multi-Objective Reinforcement Learning Q-Learning

Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning

no code implementations • 16 Nov 2022 • Kewen Ding

A variant of MORL Q-Learning incorporating global statistics is shown to outperform the baseline method in original Space Traders problem, but remains below 100 percent effectiveness in finding the find desired SER-optimal policy at the end of training.

Decision Making Multi-Objective Reinforcement Learning +2