Safe Policy Iteration


Matteo Pirotta, Marcello Restelli, Alessio Pecorino, Daniele Calandriello ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):307-315, 2013.


This paper presents a study of the policy improvement step that can be usefully exploited by approximate policy-iteration algorithms. When either the policy evaluation step or the policy improvement step returns an approximated result, the sequence of policies produced by policy iteration may not be monotonically increasing, and oscillations may occur. To address this issue, we consider safe policy improvements, i.e., at each iteration we search for a policy that maximizes a lower bound to the policy improvement w.r.t. the current policy. When no improving policy can be found the algorithm stops. We propose two safe policy-iteration algorithms that differ in the way the next policy is chosen w.r.t. the estimated greedy policy. Besides being theoretically derived and discussed, the proposed algorithms are empirically evaluated and compared with state-of-the-art approaches on some chain-walk domains and on the Blackjack card game.

Related Material