  Autonomous systems that operate around humans will likely always rely on kill
switches that stop their execution and allow them to be remote-controlled for
the safety of humans or to prevent damage to the system. It is theoretically
possible for an autonomous system with sufficient sensor and effector
capability that learn online using reinforcement learning to discover that the
kill switch deprives it of long-term reward and thus learn to disable the
switch or otherwise prevent a human operator from using the switch. This is
referred to as the big red button problem. We present a technique that prevents
a reinforcement learning agent from learning to disable the kill switch. We
introduce an interruption process in which the agent's sensors and effectors
are redirected to a virtual simulation where it continues to believe it is
receiving reward. We illustrate our technique in a simple grid world
environment.
