Why Multi-objective Reinforcement Learning?
Shimon Whiteson · 2015
We argue that multi-objective methods are underrepresented in RL research, and present three scenarios to justify the need for explicitly multi-objective approaches. Key to these scenarios is that although the utility the user derives from a policy | which is what we ultimately aim to optimize | is scalar, it is sometimes impossible, undesirable or infeasible to formulate the problem as single-objective at the moment when the policies need to be learned. We also present the case for a utility-based view of multi-objective RL, i.e., that the appropriate multi-objective solution concept should be derived from what we know about the user’s utility function, rather than axiomatically assumed to be the Pareto front. Many tasks have multiple, possibly conicting, objectives. However, while interest in