Beyond black-box advice: learning-augmented algorithms for MDPs with Q-value predictions
We study the tradeoff between consistency and robustness in the context of a single-
trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned …
trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned …
Anytime-competitive reinforcement learning with policy prior
This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP).
Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the …
Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the …
Online adaptive policy selection in time-varying systems: No-regret via contractive perturbations
We study online adaptive policy selection in systems with time-varying costs and dynamics.
We develop the Gradient-based Adaptive Policy Selection (GAPS) algorithm together with a …
We develop the Gradient-based Adaptive Policy Selection (GAPS) algorithm together with a …
Gray-box nonlinear feedback optimization
Feedback optimization enables autonomous optimality seeking of a dynamical system
through its closed-loop interconnection with iterative optimization algorithms. Among various …
through its closed-loop interconnection with iterative optimization algorithms. Among various …
Learning-Augmented Scheduling for Solar-Powered Electric Vehicle Charging
T Li - arXiv preprint arXiv:2311.05941, 2023 - arxiv.org
We tackle the complex challenge of scheduling the charging of electric vehicles (EVs)
equipped with solar panels and batteries, particularly under out-of-distribution (OOD) …
equipped with solar panels and batteries, particularly under out-of-distribution (OOD) …