  Reinforcement learning in complex environments may require supervision to
prevent the agent from attempting dangerous actions. As a result of supervisor
intervention, the executed action may differ from the action specified by the
policy. How does this affect learning? We present the Modified-Action Markov
Decision Process, an extension of the MDP model that allows actions to differ
from the policy. We analyze the asymptotic behaviours of common reinforcement
learning algorithms in this setting and show that they adapt in different ways:
some completely ignore modifications while others go to various lengths in
trying to avoid action modifications that decrease reward. By choosing the
right algorithm, developers can prevent their agents from learning to
circumvent interruptions or constraints, and better control agent responses to
other kinds of action modification, like self-damage.
