  In safety-critical applications of reinforcement learning such as healthcare
and robotics, it is often desirable to optimize risk-sensitive objectives that
account for tail outcomes rather than expected reward. We prove the first
regret bounds for reinforcement learning under a general class of
risk-sensitive objectives including the popular CVaR objective. Our theory is
based on a novel characterization of the CVaR objective as well as a novel
optimistic MDP construction.
