MORL/D

Multi-Objective Reinforcement Learning based on Decomposition. The idea of this framework is to decompose the multi-objective problem into a set of single-objective problems. The single-objective problems are then solved by a single-objective RL algorithm (or something close). There are multiple tricks which can be applied to improve the sample efficiency when compared to just sequentially solving each single-objective RL problem.

See the paper Multi-Objective Reinforcement Learning based on Decomposition for more details.