20 citations · 53 across the 15 of their papers we have counts for
4 papers · 1 filter
Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity
Philip Amortila, Dylan J. Foster, Nan Jiang +2
Real-world applications of reinforcement learning often involve environments where agents operate on complex, high-dimensional observations, but the underlying (''latent'') dynamic…
Scalable Online Exploration via Coverability
Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy
Exploration is a major challenge in reinforcement learning, especially for high-dimensional domains that require function approximation. We propose exploration objectives -- policy…
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning
Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
A pervasive phenomenon in machine learning applications is distribution shift, where training and deployment conditions for a machine learning model differ. As distribution shift t…
Harnessing Density Ratios for Online Reinforcement Learning
Philip Amortila, Dylan J. Foster, Nan Jiang +2
The theories of offline and online reinforcement learning, despite having evolved in parallel, have begun to show signs of the possibility for a unification, with algorithms and an…