document: Q-learning for risk-sensitive control We propose for risk-sensitive control of finite Markov chains a counterpart of the popular Q-learning algorithm for classical Markov decision processes . The algorithm is shown to converge with probability one to the desired solution . The proof technique is an adaptation of the o.d.e. approach for the analysis of stochastic approximation algorithms , with most of the work involved used for the analysis of the specific o.d.e.s that arise
doc_bio_tags: O O O O O O O O O O B I I O O O O O O O O B I I I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O
extractive_keyphrases: finite markov chains classical markov decision processes proof technique stochastic approximation algorithms
abstractive_keyphrases: risk-sensitive control q-learning algorithm algorithm convergence reinforcement learning algorithms dynamic programming ordinary differential equations