Gradient-Based Relational Reinforcement-Learning of Temporally Extended Policies

We consider the problem of computing general policies for decision-theoretic planning problems with temporally extended rewards. We describe a gradient-based approach to relational reinforcement-learning (RRL) of policies for that setting. In particular, the learner optimises its behaviour by acting in a set of problems drawn from a target domain. Our approach is similar to inductive policy selection because the policies learnt are given in terms of relational control-rules. These rules are generated either (1) by reasoning from a firstorder specification of the domain, or (2) more or less arbitrarily according to a taxonomic concept language. To this end the paper contributes a domain definition language for problems with temporally extended rewards, and a taxonomic concept language in which concepts and relations can be temporal. We evaluat