Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.

Each client pulls an arm and communicates with neighbors based on the graph provided by the environment.

Probabilistic modelling lies at the heart of machine learning.

The proximal policy optimization (PPO) algorithm stands as one of the most prosperous methods in the field of reinforcement learning (RL). Despite its success, the theoretical understanding of PPO remains deficient. Specifically, it is unclear whether PPO or its optimistic variants can effectively solve linear Markov decision processes (MDPs), which are arguably the simplest models in RL with function approximation.

Let k (,) be the ν = 2 .5 Matérn kernel.