most citedAdaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20203 cited

Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond

Siavash Alemzadeh, Ramin Moslemi, Ratnesh Sharma +1

In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art…

math.OC2020

Stochastic Decision-Making Model for Aggregation of Residential Units with PV-Systems and Storages

Hossein Khazaei, Ramin Moslemi, Ratnesh Sharma

Many residential energy consumers have installed photovoltaic (PV) panels and energy storage systems. These residential users can aggregate and participate in the energy markets. A…

eess.SY20192 cited

Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation

Changfu Li, Chenrui Jin, Ratnesh Sharma

Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can…

cs.LG2019

BAFFLE : Blockchain Based Aggregator Free Federated Learning

Paritosh Ramanan, Kiyoshi Nakayama

A key aspect of Federated Learning (FL) is the requirement of a centralized aggregator to maintain and update the global model. However, in many cases orchestrating a centralized a…

cs.LG2019

Energy Predictive Models with Limited Data using Transfer Learning

Ali Hooshmand, Ratnesh Sharma

In this paper, we consider the problem of developing predictive models with limited data for energy assets such as electricity loads, PV power generations, etc. We specifically inv…