633 citations · 1.5k across the 68 of their papers we have counts for
5 papers · 1 filter
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…