  We consider the problem of Approximate Dynamic Programming in relational
domains. Inspired by the success of fitted Q-learning methods in propositional
settings, we develop the first relational fitted Q-learning algorithms by
representing the value function and Bellman residuals. When we fit the
Q-functions, we show how the two steps of Bellman operator; application and
projection steps can be performed using a gradient-boosting technique. Our
proposed framework performs reasonably well on standard domains without using
domain models and using fewer training trajectories.
