Practical Risk Measures in Reinforcement Learning

Understand

Practical application of Reinforcement Learning (RL) often involves risk considerations.

• We study a generalized approximation scheme for risk measures, based on Monte-Carlo simulations, where the risk measures need not necessarily be \emph{coherent}.
• We demonstrate that, even in simple problems, measures such as the variance of the reward-to-go do not capture the risk in a satisfactory manner.
• In addition, we show how a risk measure can be derived from model's realizations.