Fitted Q-Learning for Relational Domains
By Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald E. Parr, Kristian Kersting. 2020
17 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Cynthia Rudin
- Tong Wang
- Octavio Loyola-González
- Berk Ustun
In Reinforcement Learning
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “In other relational methods policies or value functions are represented by regression trees (Džeroski et al., 1998, 2001; Das et al., 2020).”