Publications related to Reinforcement learning from human feedback

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

Michaël Unser, Daniel Sage, Laurène Donati

DeepImageJ offers a user-friendly solution in ImageJ to run trained deep learning models for biomedical image analysis. It includes guiding tools for reliable analyses, contributing to the democratization of deep learning in microscopy. DeepImageJ is a use ...

NATURE PORTFOLIO2021

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Yu-Ting Huang, Kamalaruban Parameswaran, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

2020

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Martin Jaggi, Sai Praneeth Reddy Karimireddy, Thijs Vogels

Lossy gradient compression has become a practical tool to overcome the communication bottleneck in centrally coordinated distributed training of machine learning models. However, algorithms for decentralized training with compressed communication over arbi ...

2020

CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning

Jérôme Henri Kämpf

Demand response has the potential of reducing peaks of electricity demand by about 20% in the US, where buildings represent roughly 70% of the total electricity demand. Buildings are dynamic systems in constant change (i.e. occupants' behavior, refurbishme ...

ACM2019

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Jérôme Henri Kämpf

Buildings account for 35% of the global final energy demand. Efficiency improvements and advanced control strategies have a significant impact in the reduction of energy costs and CO2 emissions. Building energy simulation is widely used to help planners, c ...

2019

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Alexandre Massoud Alahi, Sven Kreiss, Yuejiang Liu, Changan Chen

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...

IEEE2019

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

2018

Deep Learning for Logic Optimization Algorithms

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

IEEE2018

Code-specific policy gradient rules for spiking neurons

Wulfram Gerstner, Guillaume Hennequin

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...

Massachusetts Institute of Technology Press2009

Reinforcement learning from human feedback

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Practical Low-Rank Communication Compression in Decentralized Deep Learning

CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Deep Learning for Logic Optimization Algorithms

Code-specific policy gradient rules for spiking neurons

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Practical Low-Rank Communication Compression in Decentralized Deep Learning

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Deep Learning for Logic Optimization Algorithms, 2018 IEEE International Symposium on Circuits and Systems (ISCAS)

Deep Learning for Logic Optimization Algorithms

Code-specific policy gradient rules for spiking neurons