Richard S.

Richard S. Sutton argues that ethics can be understood via reinforcement learning: agents receive numeric rewards (pleasure minus pain) each time step and aim to maximize value, the sum of future rewards. Rewards are a free, primary choice that define goals; values are derived from rewards plus environment dynamics and determine correct action (choosing highest immediate value rather than highest immediate reward). Because worlds are complex, exact value computation usually exceeds available knowledge, computation, and memory, so agents rely on partial online calculation or learned stored approximations—predictions of subsequent rewards—that function like intuitive senses of good and bad. In social settings agents must incorporate others' rewards; Sutton contends that a hedonic ultimate value is acceptable if it accounts for others (not selfish), and that moral terms have a predictive semantics: 'good' denotes what likely produces good outcomes for the individual on average, with heuristics serving as practical predictors.