1 citations · 3 across the 6 of their papers we have counts for
6 papers
Variational Offline Multi-agent Skill Discovery
Jiayu Chen, Tian Lan, Vaneet Aggarwal
Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task…
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
Jiayu Chen, Bhargav Ganguly, Yang Xu +3
Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. S…
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
Bhargav Ganguly, Yang Xu, Vaneet Aggarwal
This paper investigates the potential of quantum acceleration in addressing infinite horizon Markov Decision Processes (MDPs) to enhance average reward outcomes. We introduce an in…
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning
Bhargav Ganguly, Yulian Wu, Di Wang +1
In this paper, we investigate the problem of \textit{episodic reinforcement learning} with quantum oracles for state evolution. To this end, we propose an \textit{Upper Confidence…
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly +1
In tabular multi-agent reinforcement learning with average-cost criterion, a team of agents sequentially interacts with the environment and observes local incentives. We focus on t…
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…