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Researcher profile

Vaneet Aggarwal

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Tiered cloud storage via two-stage, latency-aware bidding

    2017 · arXiv (Cornell University)

    In cloud storage, the digital data is stored in logical storage pools, backed by heterogeneous physical storage media and computing infrastructure that are managed by a Cloud Service Provider (CSP). To balance the tradeoff between …

  2. Multi-agent Deep Covering Skill Discovery

    2022 · arXiv (Cornell University)

    The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual agents, in multi-agent reinforcement learning …

  3. Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning

    2023 · arXiv (Cornell University)

    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 Bound} (UCB) based quantum algorithmic framework to facilitate learning of …

  4. Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control

    2023

    Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy with the option framework. Existing methods either overlook the causal relationship between the …

  5. Domain Adaptive Few-Shot Open-Set Learning

    2023

    Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts between domains. However, existing techniques fall short when it …

  6. Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries

    2024 · arXiv (Cornell University)

    Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead to catastrophic results. We …