Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach
Sriraam Natarajan, Saket Joshi, Prasad V. Tadepalli, Kristian Kersting, JUDE W. SHAVLIK · 2011
Imitation learning refers to the problem of learn-ing how to behave by observing a teacher in ac-tion. We consider imitation learning in relational domains, in which there is a varying number of ob-jects and relations among them. In prior work, sim-ple relational policies are learned by viewing imi-tation learning as supervised learning of a function from states to actions. For propositional worlds, functional gradient methods have been proved to be beneficial. They are simpler to implement than most existing methods, more efficient, more natu-rally satisfy common constraints on the cost func-tion, and better represent our prior beliefs about the form of the function. Building on recent gen-eralizations of functional gradient boosting to rela-tional representations, we implement a functional gradient boosting approach to imitation learning in relational domains. In particular, given a set of traces from the human teacher, our system learns a policy in the form of a set of relational regression trees that additively approximate the functional gra-dients. The use of multiple additive trees combined with relational representation allows for learning more expressive policies than what has been done before. We demonstrate the usefulness of our ap-proach in several different domains. 1