Harnessing density ratios for online reinforcement learning
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 analysis …
have begun to show signs of the possibility for a unification, with algorithms and analysis …
Exploration is harder than prediction: Cryptographically separating reinforcement learning from supervised learning
Supervised learning is often computationally easy in practice. But to what extent does this
mean that other modes of learning, such as reinforcement learning (RL), ought to be …
mean that other modes of learning, such as reinforcement learning (RL), ought to be …
Scalable Online Exploration via Coverability
Exploration is a major challenge in reinforcement learning, especially for high-dimensional
domains that require function approximation. We propose exploration objectives--policy …
domains that require function approximation. We propose exploration objectives--policy …
Efficiently Learning Markov Random Fields from Dynamics
An important task in high-dimensional statistics is learning the parameters or dependency
structure of an undirected graphical model, or Markov random field (MRF). Much of the prior …
structure of an undirected graphical model, or Markov random field (MRF). Much of the prior …
On Learning Parities with Dependent Noise
In this expository note we show that the learning parities with noise (LPN) assumption is
robust to weak dependencies in the noise distribution of small batches of samples. This …
robust to weak dependencies in the noise distribution of small batches of samples. This …