Optimistic Planning of Deterministic Systems

If one possesses a model of a controlled deterministic system, then from any state, one may consider the set of all possible reachable states starting from that state and using any sequence of actions. This forms a tree whose size is exponential in the planning time horizon. Here we ask the question: given finite computational resources (e.g. CPU time), which may not be known ahead of time, what is the best way to explore this tree, such that once all resources have been used, the algorithm would be able to propose an action (or a sequence of actions) whose performance is as close as possible to optimality? The performance with respect to optimality is assessed in terms of the regret (with respect to the sum of discounted future rewards) resulting from choosing the action returned by the algorithm instead of an optimal action. In this paper we investigate an optimistic exploration of the tree, where the most promising states are explored first, and compare this approach to a naive unif...

• Type: Conference
• Year: 2008
• Where: EWRL
• Authors: Jean-François Hren, Rémi Munos
• Keywords: Controlled Deterministic System, EWRL 2008, Machine Learning, Optimistic Exploration, Possible Reachable States