633 citations · 1.5k across the 68 of their papers we have counts for
6 papers · 1 filter
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation
Anna Winnicki, R. Srikant
We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation alg…
Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB
Zixian Yang, R. Srikant, Lei Ying
Multi-server queueing systems are widely used models for job scheduling in machine learning, wireless networks, crowdsourcing, and healthcare systems. This paper considers a multi-…
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm
Semih Cayci, Niao He, R. Srikant
Natural actor-critic (NAC) and its variants, equipped with the representation power of neural networks, have demonstrated impressive empirical success in solving Markov decision pr…
Minimax Regret for Cascading Bandits
Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai +1
Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting. For the case of unstructured rewards, w…
Finite-Time Analysis of Natural Actor-Critic for POMDPs
Semih Cayci, Niao He, R. Srikant
We consider the reinforcement learning problem for partially observed Markov decision processes (POMDPs) with large or even countably infinite state spaces, where the controller ha…
A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP
Mehrdad Moharrami, Yashaswini Murthy, Arghyadip Roy +1
We study the risk-sensitive exponential cost MDP formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of par…