Search Results for author: Desik Rengarajan

Found 7 papers, 3 papers with code

Structured Reinforcement Learning for Media Streaming at the Wireless Edge

no code implementations10 Apr 2024 Archana Bura, Sarat Chandra Bobbili, Shreyas Rameshkumar, Desik Rengarajan, Dileep Kalathil, Srinivas Shakkottai

The goal of this work is to develop and demonstrate learning-based policies for optimal decision making to determine which clients to dynamically prioritize in a video streaming setting.


Federated Ensemble-Directed Offline Reinforcement Learning

1 code implementation4 May 2023 Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil, Srinivas Shakkottai

We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control policy only using small pre-collected datasets generated according to different unknown behavior policies.

Continuous Control Ensemble Learning +4

Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

1 code implementation26 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

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)

Reinforcement Learning for Mean Field Games with Strategic Complementarities

no code implementations21 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 implementations4 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.