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Found 9 papers, 0 papers with code
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions
We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time.
Online Learning with Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks
In this paper, for the first time, we study optimal PUE attack strategies by formulating an online learning problem where the attacker needs to dynamically decide the attacking channel in each time slot based on its attacking experience.
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
We present an algorithm that achieves almost optimal pseudo-regret bounds against adversarial and stochastic bandits.
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits
If the variance of the distributions were known, one could design an optimal sampling strategy by collecting a number of independent samples per distribution that is proportional to their variance.
PinView: Implicit Feedback in Content-Based Image Retrieval
It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user.
PAC-Bayesian Analysis of Contextual Bandits
The scaling of our regret bound with the number of states (contexts) $N$ goes as $\sqrt{N I_{\rho_t}(S;A)}$, where $I_{\rho_t}(S;A)$ is the mutual information between states and actions (the side information) used by the algorithm at round $t$.
Near-optimal Regret Bounds for Reinforcement Learning
For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy.
Variational Regret Bounds for Reinforcement Learning
This is the first variational regret bound for the general reinforcement learning setting.
Autonomous exploration for navigating in non-stationary CMPs
We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change.
