170 citations · 506 across the 51 of their papers we have counts for
8 papers · 1 filter
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path
Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt +1
We consider online reinforcement learning in Mean-Field Games (MFGs). Unlike traditional approaches, we alleviate the need for a mean-field oracle by developing an algorithm that a…
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +2
In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradient (HT-PSG) algorithm to deal with the challenges of sparse rewards in continuous control problems. Sparse re…
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
Amrit Singh Bedi, Chen Fan, Alec Koppel +4
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as…
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel +4
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the set…
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation
Yulun Tian, Amrit Singh Bedi, Alec Koppel +3
We present the first distributed optimization algorithm with lazy communication for collaborative geometric estimation, the backbone of modern collaborative simultaneous localizati…
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
Qiujiang Jin, Alec Koppel, Ketan Rajawat +1
Non-asymptotic analysis of quasi-Newton methods have gained traction recently. In particular, several works have established a non-asymptotic superlinear rate of $\mathcal{O}((1/\s…