Risk-aware Q-learning for Markov decision processes

We are interested in developing reinforcement learning algorithm to tackle risk-aware sequential decision making problems. The model we investigate is a discounted infinite-horizon Markov decision processes with finite state and action spaces. Our algorithm is based on estimating a general minimax function with stochastic approximation, and we show that several risk measures fall within this form. We derive finite-time bounds for this algorithm by combining stochastic approximation with the theories of risk-aware dynamic programming. Finally, we present extensions to several variations of risk measures.