10 citations · 21 across the 40 of their papers we have counts for
13 papers · 1 filter
On The Global Convergence Of Online RLHF With Neural Parametrization
Mudit Gaur, Amrit Singh Bedi, Raghu Pasupathy +1
The importance of Reinforcement Learning from Human Feedback (RLHF) in aligning large language models (LLMs) with human values cannot be overstated. RLHF is a three-stage process t…
A Scalable Quantum Non-local Neural Network for Image Classification
Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal
Non-local operations play a crucial role in computer vision enabling the capture of long-range dependencies through weighted sums of features across the input, surpassing the const…
Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs
Washim Uddin Mondal, Vaneet Aggarwal
This paper focuses on learning a Constrained Markov Decision Process (CMDP) via general parameterized policies. We propose a Primal-Dual based Regularized Accelerated Natural Polic…
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
Vaneet Aggarwal, Washim Uddin Mondal, Qinbo Bai
Reinforcement Learning (RL) serves as a versatile framework for sequential decision-making, finding applications across diverse domains such as robotics, autonomous driving, recomm…
A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach
Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
This work examines average-reward reinforcement learning with general policy parametrization. Existing state-of-the-art (SOTA) guarantees for this problem are either suboptimal or…
Stochastic Q-learning for Large Discrete Action Spaces
Fares Fourati, Vaneet Aggarwal, Mohamed-Slim Alouini
In complex environments with large discrete action spaces, effective decision-making is critical in reinforcement learning (RL). Despite the widespread use of value-based RL approa…
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
This paper introduces a federated learning framework tailored for online combinatorial optimization with bandit feedback. In this setting, agents select subsets of arms, observe no…
Sample-Efficient Constrained Reinforcement Learning with General Parameterization
Washim Uddin Mondal, Vaneet Aggarwal
We consider a constrained Markov Decision Problem (CMDP) where the goal of an agent is to maximize the expected discounted sum of rewards over an infinite horizon while ensuring th…
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi +2
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a globa…
Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs
Swetha Ganesh, Washim Uddin Mondal, Vaneet Aggarwal
We present two Policy Gradient-based algorithms with general parametrization in the context of infinite-horizon average reward Markov Decision Process (MDP). The first one employs…
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A. Suttle, Alec Koppel +4
In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy need…
Near-perfect Coverage Manifold Estimation in Cellular Networks via conditional GAN
Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri +4
This paper presents a conditional generative adversarial network (cGAN) that translates base station location (BSL) information of any Region-of-Interest (RoI) to location-dependen…
Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
Qinbo Bai, Washim Uddin Mondal, Vaneet Aggarwal
This paper explores the realm of infinite horizon average reward Constrained Markov Decision Processes (CMDPs). To the best of our knowledge, this work is the first to delve into t…