633 citations · 1.5k across the 68 of their papers we have counts for
68 papers
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
Ronshee Chawla, Daniel Vial, Sanjay Shakkottai +1
The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $…
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games
Anna Winnicki, R. Srikant
Optimal policies in standard MDPs can be obtained using either value iteration or policy iteration. However, in the case of zero-sum Markov games, there is no efficient policy iter…
On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation
Anna Winnicki, R. Srikant
A common technique in reinforcement learning is to evaluate the value function from Monte Carlo simulations of a given policy, and use the estimated value function to obtain a new…
Spectral Clustering for Crowdsourcing with Inherently Distinct Task Types
Saptarshi Mandal, Seo Taek Kong, Dimitrios Katselis +1
The Dawid-Skene model is the most widely assumed model in the analysis of crowdsourcing algorithms that estimate ground-truth labels from noisy worker responses. In this work, we a…
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Many policy-based reinforcement learning (RL) algorithms can be viewed as instantiations of approximate policy iteration (PI), i.e., where policy improvement and policy evaluation…
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…