L. Busoniu, R. Munos, B. De Schutter, and R. Babuska, "Optimistic planning for sparsely stochastic systems," Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), Paris, France, pp. 48-55, Apr. 2011.
We propose an online planning algorithm for finite-action, sparsely stochastic Markov decision processes, in which the random state transitions can only end up in a small number of possible next states. The algorithm builds a planning tree by iteratively expanding states, where each expansion exploits sparsity to add all possible successor states. Each state to expand is actively chosen to improve the knowledge about action quality, and this allows the algorithm to return a good action after a strictly limited number of expansions. More specifically, the active selection method is optimistic in that it chooses the most promising states first, so the novel algorithm is called optimistic planning for sparsely stochastic systems. We note that the new algorithm can also be seen as model-predictive (receding-horizon) control. The algorithm obtains promising numerical results, including the successful online control of a simulated HIV infection with stochastic drug effectiveness.