Fitted Q-Learning for Relational DomainsSrijita Das; Sriraam Natarajan; Kaushik Roy; Ronald Parr; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2006.05595, Pages 0-10, arXiv, 2020.
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.