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Wesley Suttle

3 accepted papers

2024

Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems

AISTATS 2024poster

We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advances at the intersection of control and RL follow a…

2023

Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic

ICML 2023poster

Many existing reinforcement learning (RL) methods employ stochastic gradient iteration on the back end, whose stability hinges upon a hypothesis that the data-generating process mixes exponentially fast with a rate parameter that appears in the step-size selection. Unfortunately, this assumption is…

Cited by 14SourcePDFScholar
2021

Reinforcement Learning for Cost-Aware Markov Decision Processes

ICML 2021spotlight

Ratio maximization has applications in areas as diverse as finance, reward shaping for reinforcement learning (RL), and the development of safe artificial intelligence, yet there has been very little exploration of RL algorithms for ratio maximization. This paper addresses this deficiency by introdu…

Cited by 11SourcePDFScholar