no code implementations • 17 May 2016 • Aravind Srinivas, Ramnandan Krishnamurthy, Peeyush Kumar, Balaraman Ravindran
This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states.
no code implementations • 19 Mar 2017 • Peeyush Kumar, Wolf Kohn, Zelda B. Zabinsky
Many applications require solving non-linear control problems that are classically not well behaved.
no code implementations • 19 Mar 2017 • Peeyush Kumar, Doina Precup
Deliberating on large or continuous state spaces have been long standing challenges in reinforcement learning.
no code implementations • 3 Jul 2021 • Peeyush Kumar, Ayushe Gangal, Sunita Kumari
The network also slender the occurrences of false negative cases by employing a high threshold value, thus aids in curbing the spread of the disease and gives an accuracy of 100% for successfully predicting COVID-19 among the chest x-rays of patients affected with COVID-19, bacterial and viral pneumonia.
no code implementations • 6 Jul 2021 • Ayushe Gangal, Peeyush Kumar, Sunita Kumari, Aditya Kumar
This chapter aims to provide next-level understanding of the problems of the world and the solutions available to those problems, which lie very well within the domain of neural computing, and at the same time are intelligent in their approach, to invoke a sense of innovation among the educationalists, researchers, academic professionals, students and people concerned, by highlighting the work done by major researchers and innovators in this field and thus, encouraging the readers to develop newer and more advanced techniques for the same.
no code implementations • 8 Jul 2021 • Ayushe Gangal, Peeyush Kumar, Sunita Kumari
In the following paper, we have combined the various basic functionalities provided by the NumPy library and OpenCv library, which is an open source for Computer Vision applications, like conversion of colored images to grayscale, calculating threshold, finding contours and using those contour points to take perspective transform of the image inputted by the user, using Python version 3. 7.
no code implementations • 5 May 2022 • Sarah H. Q. Li, Lillian J. Ratliff, Peeyush Kumar
Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these domains: 1) network-structured player interactions, 2) pair-wise mixed cooperation and competition among players, and 3) limited global information toward individual decision-making.
no code implementations • 4 Mar 2023 • Peeyush Kumar, Andrew Nelson, Zerina Kapetanovic, Ranveer Chandra
Farms produce hundreds of thousands of data points on the ground daily.
no code implementations • 13 Jun 2023 • Lucien Werner, Peeyush Kumar
It validates the adaptability of the learning framework with various storage models and shows the effectiveness of RL in a complex energy optimization setting, in the context of multi-market bidding, probabilistic forecasts, and accurate storage component models.
no code implementations • 20 Jun 2023 • Peeyush Kumar
Reinforcement Learning (RL) models have continually evolved to navigate the exploration - exploitation trade-off in uncertain Markov Decision Processes (MDPs).
no code implementations • 9 Dec 2023 • Ananta Mukherjee, Peeyush Kumar, Boling Yang, Nishanth Chandran, Divya Gupta
To tackle this challenge, we propose a game-theoretic, privacy-preserving mechanism, utilizing a secure multi-party computation (MPC) framework in MARL settings.
Multi-agent Reinforcement Learning Policy Gradient Methods +2
no code implementations • 5 Jan 2024 • Iman Deznabi, Peeyush Kumar, Madalina Fiterau
Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations.