A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents
This addresses the challenge of ensuring ethical behavior in RL agents for designers, though it appears incremental as it builds on existing human policy integration methods.
The paper tackles the problem of designing reinforcement learning agents that behave ethically by integrating human policy with RL policy, resulting in agents that achieve target objectives with reduced ethical violations.
This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically. Our model allows the designers of RL agents to solely focus on the task to achieve, without having to worry about the implementation of multiple trivial ethical patterns to follow. Based on the assumption that the majority of human behavior, regardless which goals they are achieving, is ethical, our design integrates human policy with the RL policy to achieve the target objective with less chance of violating the ethical code that human beings normally obey.