113 citations · 129 across the 18 of their papers we have counts for
18 papers
Enhancing Policy Gradient with the Polyak Step-Size Adaption
Yunxiang Li, Rui Yuan, Chen Fan +4
Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL). Renowned for its convergence guarantees and stability compared to other…
FRESCO: Federated Reinforcement Energy System for Cooperative Optimization
Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin Takáč
The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part…
Generalized Policy Learning for Smart Grids: FL TRPO Approach
Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth +1
The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable abil…
Reinforcement Learning for Solving Stochastic Vehicle Routing Problem with Time Windows
Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal +1
This paper introduces a reinforcement learning approach to optimize the Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on reducing travel costs in goods deli…
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
Petr Ostroukhov, Aigerim Zhumabayeva, Chulu Xiang +3
This paper presents a novel adaptation of the Stochastic Gradient Descent (SGD), termed AdaBatchGrad. This modification seamlessly integrates an adaptive step size with an adjustab…
SANIA: Polyak-type Optimization Framework Leads to Scale Invariant Stochastic Algorithms
Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov +2
Adaptive optimization methods are widely recognized as among the most popular approaches for training Deep Neural Networks (DNNs). Techniques such as Adam, AdaGrad, and AdaHessian…