We argue that these properties are satisfied in many continuous state-action Markov decision processes.

We present a theoretical result demonstrating the strong dependency of suboptimality on the number of Monte Carlo samples taken per Bellman target calculation.

We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.