  In Multi-objective Reinforcement Learning (MORL) agents are tasked with
optimising decision-making behaviours that trade-off between multiple, possibly
conflicting, objectives. MORL based on decomposition is a family of solution
methods that employ a number of utility functions to decompose the
multi-objective problem into individual single-objective problems solved
simultaneously in order to approximate a Pareto front of policies. We focus on
the case of linear utility functions parameterised by weight vectors w. We
introduce a method based on Upper Confidence Bound to efficiently search for
the most promising weight vectors during different stages of the learning
process, with the aim of maximising the hypervolume of the resulting Pareto
front. The proposed method is shown to outperform various MORL baselines on
Mujoco benchmark problems across different random seeds. The code is online at:
https://github.com/SYCAMORE-1/ucb-MOPPO.
