We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to problems where a priori action discretization is inadequate. The algorithm is an extension of a variable resolution technique that works for problems with continuous states and discrete actions [6]. Results are given that indicate that JoSTLe is a promising step toward reinforcement learning in a fully continuous domain.
Christopher K. Monson, David Wingate, Kevin D. Sep