Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization

no code implementations • 14 Mar 2024 • Tianqi Zheng, Nicolas Loizou, Pengcheng You, Enrique Mallada

Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e. g., in bilinear settings.

Closed-Loop Motion Planning for Differentially Flat Systems: A Time-Varying Optimization Framework

no code implementations • 19 Oct 2023 • Tianqi Zheng, John W. Simpson-Porco, Enrique Mallada

The standard approach to combine these methodologies comprises an offline/open-loop stage, planning, that designs a feasible and safe trajectory to follow, and an online/closed-loop stage, tracking, that corrects for unmodeled dynamics and disturbances.

Motion Planning

Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics

no code implementations • 3 Dec 2022 • Tianqi Zheng, Pengcheng You, Enrique Mallada

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requirements in secondary cumulative reward constraints.

reinforcement-learning Reinforcement Learning (RL)