libGroomRL: Reinforcement Learning for Jets
@article{Carrazza2019libGroomRLRL,
title={libGroomRL: Reinforcement Learning for Jets},
author={Stefano Carrazza and Fr{\'e}d{\'e}ric A. Dreyer},
journal={arXiv: Data Analysis, Statistics and Probability},
year={2019}
}In these proceedings, we present a library allowing for straightforward calls in C++ to jet grooming algorithms trained with deep reinforcement learning. The RL agent is trained with a reward function constructed to optimize the groomed jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved…
References
SHOWING 1-10 OF 19 REFERENCES
Jet Grooming through Reinforcement Learning
- Computer ScienceJournal of Physics: Conference Series
- 2019
It is shown that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects.
Dueling Network Architectures for Deep Reinforcement Learning
- Computer ScienceICML
- 2016
This paper presents a new neural network architecture for model-free reinforcement learning that leads to better policy evaluation in the presence of many similar-valued actions and enables the RL agent to outperform the state-of-the-art on the Atari 2600 domain.
Playing Atari with Deep Reinforcement Learning
- Computer ScienceArXiv
- 2013
This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
Jet-images — deep learning edition
- Physics, Computer Science
- 2015
This interplay between physicallymotivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.
Deep Reinforcement Learning with Double Q-Learning
- Computer ScienceAAAI
- 2016
This paper proposes a specific adaptation to the DQN algorithm and shows that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.
Human-level control through deep reinforcement learning
- Computer ScienceNature
- 2015
This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
How much information is in a jet?
- Physics
- 2017
A bstractMachine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different…
Adam: A Method for Stochastic Optimization
- Computer ScienceICLR
- 2015
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures
- Computer ScienceICML
- 2013
This work proposes a meta-modeling approach to support automated hyperparameter optimization, with the goal of providing practical tools that replace hand-tuning with a reproducible and unbiased optimization process.