QuaRL: Quantization for Fast and Environmentally Sustainable
Reinforcement Learning

QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning

2 October 2019

Srivatsan Krishnan

Maximilian Lam

Sharad Chitlangia

Gabriel Barth-Maron

Aleksandra Faust

Vijay Janapa Reddi

Papers citing "QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning"

5 / 5 papers shown

Title
HEPPO: Hardware-Efficient Proximal Policy Optimization -- A Universal Pipelined Architecture for Generalized Advantage Estimation Hazem Taha Ameer M. S. Abdelhadi 35 1 0 22 Jan 2025
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models Heng Lu Mehdi Alemi Reza Rawassizadeh 34 1 0 05 Jul 2024
Neural Network Compression for Reinforcement Learning Tasks Dmitry A. Ivanov D. Larionov Oleg V. Maslennikov V. Voevodin OffRL AI4CE 43 0 0 13 May 2024
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems Kailash Gogineni Sai Santosh Dayapule Juan Gómez Luna Karthikeya Gogineni Peng Wei Tian-Shing Lan Mohammad Sadrosadati Onur Mutlu Guru Venkataramani 47 10 0 07 May 2024
ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G Ahmed Benfaid N. Adem Abdurrahman Elmaghbub 13 1 0 13 Oct 2022