Variable resolution discretization in the joint space

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.