  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.
