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Learning to Control under Time-Varying Environment
To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning
This paper presents a model-free reinforcement learning (RL) algorithm to solve the risk-averse optimal control (RAOC) problem for discrete-time nonlinear systems.
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