no code implementations • 4 Feb 2023 • Pouya Hamadanian, Arash Nasr-Esfahany, Malte Schwarzkopf, Siddartha Sen, Mohammad Alizadeh
We present Locally Constrained Policy Optimization (LCPO), an online RL approach that combats CF by anchoring policy outputs on old experiences while optimizing the return on current experiences.
no code implementations • 14 Jan 2022 • Pouya Hamadanian, Malte Schwarzkopf, Siddartha Sen, Mohammad Alizadeh
Such agents must explore and learn new environments, without hurting the system's performance, and remember them over time.
2 code implementations • 3 Oct 2018 • Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh
Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms.
no code implementations • ICLR 2019 • Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, Mohammad Alizadeh
We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system.