Fouad Bahrpeyma

A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning

A unified analytical framework for comparing dynamic reward shaping and neighbouring adaptive reward mechanisms is introduced, which distinguishes parametric revision from state-dependent variation, separates additive shaping from reward replacement and reward-adjacent guidance, and organises existing methods along temporal, informational, and theoretical dimensions.