  Recently, there has been a surge in interest in safe and robust techniques
within reinforcement learning (RL). Current notions of risk in RL fail to
capture the potential for systemic failures such as abrupt stoppages from
system failures or surpassing of safety thresholds and the appropriate
responsive controls in such instances. We propose a novel approach to risk
minimisation within RL in which, in addition to taking actions that maximise
its expected return, the controller learns a policy that is robust against
stoppages due to an adverse event such as an abrupt failure. The results of the
paper cover fault-tolerant control in \textit{worst-case scenarios} under
random stopping and optimal stopping, all in unknown environments. By
demonstrating that the class of problems is represented by a variant of
stochastic games, we prove the existence of a solution which is a unique fixed
point equilibrium of the game and characterise the optimal controller
behaviour. We then introduce a value function approximation algorithm that
converges to the solution through simulation in unknown environments.
