Learning Graphical Models
Bayesian Bellman Operators Matthew Fellows Kristian Hartikainen Shimon Whiteson Department of Computer Science University of Oxford
We demonstrate that BootDQNprior+'s lagged target parameters, which are essential to its performance, arise from applying approximate inference to the BBO posterior.
Variational inference via Wasserstein gradient flows
Below, we proceed to describe our contributions in greater detail. Our framework builds upon the seminal work of Jordan et al. [1998], which introduced the celebrated
Linear Uncertainty Quantification of Graphical Model Inference
Graphical models, capable of representing data with complex dependencies, are widely used across domains.
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints
Real-world reinforcement learning (RL) applications often come with possibly infinite state and action space, and in such a situation classical RL algorithms developed in the tabular setting are not applicable anymore. A popular approach to overcoming this issue is by applying function approximation techniques to the underlying structures of the Markov decision processes (MDPs).
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.
Bayesian Optimistic Optimization: Optimistic
In this paper, we consider the RL in Markov decision processes (MDPs), where the agent observes the state of the environment at each timestep and makes decisions accordingly.