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cs.LG2025
Near-Optimal Sample Complexity for Iterated CVaR Reinforcement Learning with a Generative Model
Zilong Deng, Simon Khan, Shaofeng Zou
In this work, we study the sample complexity problem of risk-sensitive Reinforcement Learning (RL) with a generative model, where we aim to maximize the Conditional Value at Risk (…
cs.LG2024★ 1 cited
Non-Asymptotic Analysis for Single-Loop (Natural) Actor-Critic with Compatible Function Approximation
Yudan Wang, Yue Wang, Yi Zhou +1
Actor-critic (AC) is a powerful method for learning an optimal policy in reinforcement learning, where the critic uses algorithms, e.g., temporal difference (TD) learning with func…