Search Results for author:
Found 6 papers, 0 papers with code
Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm
We characterize the regret of ExpRb as a function of the corruption budget and show that for the case of a known corruption budget, the regret of ExpRb is tight.
Improved Exploration in Factored Average-Reward MDPs
We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP).
Tightening Exploration in Upper Confidence Reinforcement Learning
In pursuit of practical efficiency, we present UCRL3, following the lines of UCRL2, but with two key modifications: First, it uses state-of-the-art time-uniform concentration inequalities to compute confidence sets on the reward and (component-wise) transition distributions for each state-action pair.
Model-Based Reinforcement Learning Exploiting State-Action Equivalence
In the case of an unknown equivalence structure, we show through numerical experiments that C-UCRL combined with ApproxEquivalence outperforms UCRL2 in ergodic MDPs.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Multiple Markov Chains via Adaptive Allocation
We study the problem of learning the transition matrices of a set of Markov chains from a single stream of observations on each chain.
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs
The problem of reinforcement learning in an unknown and discrete Markov Decision Process (MDP) under the average-reward criterion is considered, when the learner interacts with the system in a single stream of observations, starting from an initial state without any reset.
