Multi-objectivization and ensembles of shapings in reinforcement learning

In this paper, we argue for the combination of multi-objectivization and ensemble techniques as a powerful tool to boost solving performance in reinforcement learning. We inject various pieces of heuristic information through reward shaping, creating several distinct enriched reward signals, which can strategically be combined using ensemble techniques to reduce sample complexity. We provide theoretical guarantees and demonstrate the potential of the approach with a range of experiments.