Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

no code implementations • 2 Nov 2022 • Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu, Mengdi Wang, Vijay Vittal, P. R. Kumar, Srinivas Shakkottai, Yi Cui

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation.

Decision Making

Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

1 code implementation • 26 Sep 2022 • Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai

Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy.

Meta Reinforcement Learning reinforcement-learning +1

OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation

1 code implementation • 6 Mar 2022 • Rayan El Helou, Kiyeob Lee, Dongqi Wu, Le Xie, Srinivas Shakkottai, Vijay Subramanian

This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms.

Decision Making

Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration

1 code implementation • ICLR 2022 • Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh, Dileep Kalathil, Srinivas Shakkottai

We demonstrate the superior performance of our algorithm over state-of-the-art approaches on a number of benchmark environments with sparse rewards and censored state.

reinforcement-learning Reinforcement Learning (RL)

DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning

1 code implementation • 1 Dec 2021 • Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-Francois Chamberland

Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations.

reinforcement-learning Reinforcement Learning (RL) +2

NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL

1 code implementation • NeurIPS 2021 • Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai

This paper proposes NeurWIN, a neural Whittle index network that seeks to learn the Whittle indices for any restless bandits by leveraging mathematical properties of the Whittle indices.

Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs

no code implementations • 1 Aug 2020 • Aria HasanzadeZonuzy, Archana Bura, Dileep Kalathil, Srinivas Shakkottai

Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints.

reinforcement-learning Reinforcement Learning (RL)

Reinforcement Learning for Mean Field Games with Strategic Complementarities

no code implementations • 21 Jun 2020 • Kiyeob Lee, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai

We introduce a natural refinement to the equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE), which allows agents to employ a measure of randomization while accounting for the impact of such randomization on their payoffs.

reinforcement-learning Reinforcement Learning (RL)

QFlow: A Learning Approach to High QoE Video Streaming at the Wireless Edge

no code implementations • 4 Jan 2019 • Rajarshi Bhattacharyya, Archana Bura, Desik Rengarajan, Mason Rumuly, Bainan Xia, Srinivas Shakkottai, Dileep Kalathil, Ricky K. P. Mok, Amogh Dhamdhere

The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose.