Adaptive Primal-Dual Method for Safe Reinforcement Learning

no code implementations • 1 Feb 2024 • Weiqin Chen, James Onyejizu, Long Vu, Lan Hoang, Dharmashankar Subramanian, Koushik Kar, Sandipan Mishra, Santiago Paternain

In this paper, we propose, analyze and evaluate adaptive primal-dual (APD) methods for SRL, where two adaptive LRs are adjusted to the Lagrangian multipliers so as to optimize the policy in each iteration.

reinforcement-learning Safe Reinforcement Learning

AutoDOViz: Human-Centered Automation for Decision Optimization

no code implementations • 19 Feb 2023 • Daniel Karl I. Weidele, Shazia Afzal, Abel N. Valente, Cole Makuch, Owen Cornec, Long Vu, Dharmashankar Subramanian, Werner Geyer, Rahul Nair, Inge Vejsbjerg, Radu Marinescu, Paulito Palmes, Elizabeth M. Daly, Loraine Franke, Daniel Haehn

AutoDOViz seeks to lower the barrier of entry for data scientists in problem specification for reinforcement learning problems, leverage the benefits of AutoDO algorithms for RL pipeline search and finally, create visualizations and policy insights in order to facilitate the typical interactive nature when communicating problem formulation and solution proposals between DO experts and domain experts.

AutoML reinforcement-learning +1

AutoAI-TS: AutoAI for Time Series Forecasting

no code implementations • 24 Feb 2021 • Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculin, Petros Zerfos

We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset.

Benchmarking BIG-bench Machine Learning +3