AMULED: Addressing Moral Uncertainty using Large language models for Ethical Decision-making
Sachit Mahajan
IntroductionWe address moral uncertainty in reinforcement learning (RL) by proposing a framework that integrates multiple ethical theories into decision-making. Existing approaches rely on single moral frameworks or handcrafted rewards, limiting scalability and failing to capture moral pluralism. We introduce AMULED, a task-agnostic ethical layer that refines a pre-trained RL agent using large language models (LLMs) to provide multi-perspective moral feedback.MethodsFollowing initial training, t