Alessandro Lazaric, Marcello Restelli, Andrea Bonarini
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforce- ment Learning algorithms to continuous state problems, the same techniques can be hardly extended to continuous action spaces, where, besides the computation of a good approximation of the value function, a fast method for the identiﬁcation of the highest-valued action is needed. In this paper, we propose a novel actor-critic approach in which the policy of the actor is estimated through sequential Monte Carlo methods. The importance sampling step is performed on the basis of the values learned by the critic, while the resampling step modiﬁes the actor’s policy. The proposed approach has been empirically compared to other learning algo- rithms into several domains; in this paper, we report results obtained in a control problem consisting of steering a boat across a river.