The Need for MORE: Need Systems as Non-Linear Multi-Objective Reinforcement Learning

Both biological and artificial agents need to coordinate their behavior to suit various needs at the same time. Reconciling conflicts of different needs and contradictory interests such as self-preservation and curiosity is the central difficulty arising in the design and modelling of need and value systems. Current models of multi-objective reinforcement learning do either not provide satisfactory power to describe such conflicts, or lack the power to actually resolve them. This paper aims to promote a clear understanding of these limitations, and to overcome them with a theory-driven approach rather than ad hoc solutions. The first contribution of this paper is the development of an example that demonstrates previous approaches' limitations concisely. The second contribution is a new, non-linear objective function design, MORE, that addresses these and leads to a practical algorithm. Experiments show that standard RL methods fail to grasp the nature of the problem and ad-hoc solutions struggle to describe consistent preferences. MORE consistently learns a highly satisfactory solution that balances contradictory needs based on a consistent notion of optimality.