activity
20192021
most citedMulti-Agent Multi-Armed Bandits with Limited Communication

16 citations · 17 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2021

Communication Efficient Parallel Reinforcement Learning

Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal

We consider the problem where agents interact with identical and independent environments with states and actions using reinforcement learning for rounds. The a…

cs.LG202116 cited

Multi-Agent Multi-Armed Bandits with Limited Communication

Mridul Agarwal, Vaneet Aggarwal, Kamyar Azizzadenesheli

We consider the problem where agents collaboratively interact with an instance of a stochastic arm bandit problem for . The agents aim to simultaneously minimize t…

cs.LG20201 cited

Blind Decision Making: Reinforcement Learning with Delayed Observations

Mridul Agarwal, Vaneet Aggarwal

Reinforcement learning typically assumes that the state update from the previous actions happens instantaneously, and thus can be used for making future decisions. However, this ma…

math.OC2019

Escaping Saddle Points for Zeroth-order Nonconvex Optimization using Estimated Gradient Descent

Qinbo Bai, Mridul Agarwal, Vaneet Aggarwal

Gradient descent and its variants are widely used in machine learning. However, oracle access of gradient may not be available in many applications, limiting the direct use of grad…

cs.IT2019

Encoders and Decoders for Quantum Expander Codes Using Machine Learning

Sathwik Chadaga, Mridul Agarwal, Vaneet Aggarwal

Quantum key distribution (QKD) allows two distant parties to share encryption keys with security based on laws of quantum mechanics. In order to share the keys, the quantum bits ha…

cs.LG2019

Reinforcement Learning for Mean Field Game

Mridul Agarwal, Vaneet Aggarwal, Arnob Ghosh +1

Stochastic games provide a framework for interactions among multiple agents and enable a myriad of applications. In these games, agents decide on actions simultaneously, the state…