44 citations · 238 across the 35 of their papers we have counts for
4 papers · 2 filters
Unsupervised model-free representation learning
Daniil Ryabko
Numerous control and learning problems face the situation where sequences of high-dimensional highly dependent data are available but no or little feedback is provided to the learn…
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning
Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner +1
We consider an agent interacting with an environment in a single stream of actions, observations, and rewards, with no reset. This process is not assumed to be a Markov Decision Pr…
Selecting the State-Representation in Reinforcement Learning
Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko
The problem of selecting the right state-representation in a reinforcement learning problem is considered. Several models (functions mapping past observations to a finite set) of t…
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning
Ronald Ortner, Daniil Ryabko
We derive sublinear regret bounds for undiscounted reinforcement learning in continuous state space. The proposed algorithm combines state aggregation with the use of upper confide…