2007
NeurIPS
NeurIPS 2007
Online Linear Regression and Its Application to Model-Based Reinforcement Learning
Abstract
We provide a provably efficient algorithm for learning Markov Decision Processes (MDPs) with continuous state and action spaces in the online setting. Specifically, we take a model-based approach and show that a special type of online linear regression allows us to learn MDPs with (possibly kernalized) linearly parameterized dynamics. This result builds on Kearns and Singh's work that provides a provably efficient algorithm for finite state MDPs. Our approach is not restricted to the linear setting, and is applicable to other classes of continuous MDPs.
🌱
Topic Pioneer
- Model-Based RL
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Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
📈
Trend Setter
- Model-Based RL
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Hot Topic Early Bird
- markov decision process
Authors
Topics
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Optimization
Reinforcement Learning > Methods > Deep RL
Machine Learning > Learning Types > Online Learning
Machine Learning > Learning Types > Reinforcement Learning
Artificial Intelligence > Core AI > Reinforcement Learning
Machine Learning > Learning Types > Model-Based RL
Reinforcement Learning > Methods > Model-Based RL