5 citations · 5 across the 6 of their papers we have counts for
11 papers
Risk-Sensitive Reinforcement Learning with Smoothed Quantile Objectives
Mohammad Alipour-Vaezi, Huaiyang Zhong, Sajad Khodadadian
Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensitivity of the objective funct…
Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes
Asha Barua, Sajad Khodadadian
Natural Policy Gradient (NPG) is a well-established Reinforcement Learning algorithm that underlies widely used methods such as Trust Region Policy Optimization and Proximal Policy…
Leveraging High-Fidelity Digital Models and Reinforcement Learning for Mission Engineering: A Case Study of Aerial Firefighting Under Perfect Information
İbrahim Oğuz Çetinkaya, Sajad Khodadadian, Taylan G. Topcu
As systems engineering (SE) objectives evolve from design and operation of monolithic systems to complex System of Systems (SoS), the discipline of Mission Engineering (ME) has eme…
Optimistic Reinforcement Learning with Quantile Objectives
Mohammad Alipour-Vaezi, Huaiyang Zhong, Kwok-Leung Tsui +1
Reinforcement Learning (RL) has achieved tremendous success in recent years. However, the classical foundations of RL do not account for the risk sensitivity of the objective funct…
Tail Distribution of Regret in Optimistic Reinforcement Learning
Sajad Khodadadian, Mehrdad Moharrami
We derive instance-dependent tail bounds for the regret of optimism-based reinforcement learning in finite-horizon tabular Markov decision processes with unknown transition dynamic…
A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging
Sajad Khodadadian, Martin Zubeldia
Polyak-Ruppert averaging is a widely used technique to achieve the optimal asymptotic variance of stochastic approximation (SA) algorithms, yet its high-probability performance gua…