Reinforcement learning (RL) is a version of stochastic control in which the system dynamics are unknown (up to the type of dynamics such as Markov chains or diffusion processes). There has been an upsurge of interest in RL for (continuous-time) controlled diffusions in recent years. In this talk I will highlight the latest developments on theory and algorithms arising from this study, including entropy regularized exploratory formulation, policy evaluation, policy gradient, q-learning, and regret analysis. Time permitting, I will also discuss applications to mathematical finance and generative AI.
The Munich Mathematical Calendar lists mathematical seminars, lectures and other mathematical events in the Munich area. People from TUM, LMU and the University of the Federal Armed Forces in Munich can add data.
Christian Ludwig
Lehrstuhl M3, Fakultät für Mathematik
Boltzmannstraße 3
85748 Garching
email: ludwig@ma.tum.de