Fitted Q-Learning for Relational Domains

Abstract

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.

10 pages, 12 figures

References in corpus (1)

• Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

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