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cs.LG2024
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…
cs.LG2024
A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning
Nan Jiang
This note clarifies some confusions (and perhaps throws out more) around model-based reinforcement learning and their theoretical understanding in the context of deep RL. Main topi…
cs.LG2024
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…