Trial without Error: Towards Safe Reinforcement Learning via Human Intervention

TL;DR

This paper explores a method to ensure safe reinforcement learning by using human intervention and imitation learning to prevent catastrophic actions during training, demonstrating success in simple scenarios but highlighting scalability challenges.

Contribution

It formalizes human intervention in RL and proposes a supervised learning approach to imitate intervention decisions, reducing human effort and improving safety in training.

Findings

Successfully prevented all catastrophes in simple Atari scenarios

Supervised imitation reduced human labor compared to manual intervention

Scalability issues arise with more complex environments and adversarial examples

Abstract

AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to avoid actions that could cause serious harm. How can an AI system explore and learn without making a single mistake that harms humans or otherwise causes serious damage? For model-free reinforcement learning, having a human "in the loop" and ready to intervene is currently the only way to prevent all catastrophes. We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions. We evaluate this scheme on Atari games, with a Deep RL agent being overseen by a human for four hours. When the class of catastrophes is simple, we are able to prevent all catastrophes without affecting the agent's learning (whereas an RL baseline fails due to catastrophic forgetting). However, this scheme is less successful when catastrophes are more complex: it reduces but does not eliminate catastrophes and the supervised learner fails on adversarial examples found by the agent. Extrapolating to more challenging environments, we show that our implementation would not scale (due to the infeasible amount of human labor required). We outline extensions of the scheme that are necessary if we are to train model-free agents without a single catastrophe.