Reinforcement Learning in Quasi-Continuous Time

Reinforcement learning (RL) is used here as a tool for control systems optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet the discretization may be arbitrarily fine. Within the proposed algorithm, a piece of information that leads to a policy improvement, is inferred from an experiment that lasts for several consecutive steps, rather than from a single step, as in more traditional RL methods. Simulations reveal that the algorithm is able to optimize the control policies for plants for which it is very difficult to apply the traditional methods