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arXiv · 2006.05595

Fitted Q-Learning for Relational Domains

Srijita Das·Sriraam Natarajan·Kaushik Roy·Ronald Parr·Kristian Kersting
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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.

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Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting. 2020-06-10. Fitted Q-Learning for Relational Domains. https://arxiv.org/abs/2006.05595

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