Diederik M. Roijers
Abstract
Decision making is hard. It often requires reasoning about uncertain environments, partial observability and action spaces that are too large to enumerate. In such tasks decision-theoretic agents can often assist us. In most research on decision-theoretic agents, the desirability of actions and their effects is codified in a scalar reward function. However, many real-world decision problems have multiple objectives. In such cases the problem is more naturally expressed using a vector-valued reward function, leading to a multi-objective decision problem (MODP).
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