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A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
[article]
2016
arXiv
pre-print
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Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator ...
Interestingly, maximum entropy IRL is a special case of an energy-based model. ...
Acknowledgments The authors would like to thank Ian Goodfellow and Joan Bruna for insightful discussions. ...
arXiv:1611.03852v3
fatcat:6igqsnwwkrbbtbfnsshkvdfjby
Connecting Generative Adversarial Networks and Actor-Critic Methods
[article]
2017
arXiv
pre-print
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Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. ...
We review the strategies for stabilizing training for each class of models, both those that generalize between the two and those that are particular to that model. ...
It has also been shown in [35] that generative adversarial networks have a close connection with maximum entropy inverse reinforcement learning. ...
arXiv:1610.01945v3
fatcat:2gcikcjkvngbpe3ej3y4bm4onu
Deep Generative Models with Learnable Knowledge Constraints
[article]
2018
arXiv
pre-print
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In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. ...
Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models. ...
Acknowledgment This material is based upon work supported by the National Science Foundation grant IIS1563887. ...
arXiv:1806.09764v2
fatcat:6apd74tnlfftjpbkeyjglim3uy
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
[article]
2017
arXiv
pre-print
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Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of user-preferred items. ...
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. ...
BACKGROUND 2.1 Generative Adversarial Networks Generative adversarial networks [9] is a class of generative models that learns by game theoretic competition between a generator G and a discriminator ...
arXiv:1706.09200v1
fatcat:j7bep3vgurcend7axrjk5i3o3m
Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems
[article]
2018
arXiv
pre-print
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This paper introduces Adversarial Resilience Learning (ARL), a concept to model, train, and analyze artificial neural networks as representations of competitive agents in highly complex systems. ...
We provide the constitutive nomenclature of ARL and, based on it, the description of experimental setups and results of a preliminary implementation of ARL in simulated power systems. ...
A modern application of unsupervised learning has emerged in the concept of Generative Adversarial Networks (GANs). Here, one network, called the generator network, creates solution candidatesi. e. ...
arXiv:1811.06447v1
fatcat:qapzyhrurzfflixbgzsqlbcsyy
Non-Adversarial Imitation Learning and its Connections to Adversarial Methods
[article]
2020
arXiv
pre-print
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Many modern methods for imitation learning and inverse reinforcement learning, such as GAIL or AIRL, are based on an adversarial formulation. ...
We address these problems by proposing a framework for non-adversarial imitation learning. ...
Adversarial Inverse Reinforcement Learning AIRL is based on an adversarial formulation and models the reward function r θ as a neural network which is trained as a special type of discriminator. ...
arXiv:2008.03525v1
fatcat:hgd5t5i5hfbqjh2yhym42jmffu
Intelligent on-demand design of phononic metamaterials
2022
Nanophotonics
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Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design ...
Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures ...
An inverse design network connected to a forward modeling network. ...
doi:10.1515/nanoph-2021-0639
fatcat:tonxwxrztvhudmgztlx2h7kwre
Reparameterized Variational Divergence Minimization for Stable Imitation
[article]
2020
arXiv
pre-print
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We contribute a reparameterization trick for adversarial imitation learning to alleviate the optimization challenges of the promising f-divergence minimization framework. ...
We unfortunately find that f-divergence minimization through reinforcement learning is susceptible to numerical instabilities. ...
., and Levine, S. A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models. arXiv preprint arXiv:1611.03852, 2016a. ...
arXiv:2006.10810v1
fatcat:zobyenu2rbfqfbjlk22yhus74u
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
[article]
2018
arXiv
pre-print
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In this paper, we aim to give a review of recent developments of modern deep learning based approaches, i.e. Deep Generative Models, for Face Aging task. ...
Face Aging has raised considerable attentions and interest from the computer vision community in recent years. ...
DEEP LEARNING (DL), LOG-LIKELIHOOD (LL), INVERSE REINFORCEMENT LEARNING (IRL), PROBABILISTIC GRAPHICAL MODELS (PGM), ADVERSARIAL (ADV) Method
Approach
Architecture
Loss
Function
Non
-Linearity ...
arXiv:1802.08726v1
fatcat:vomix3nu6nfsdmnbfhv6ivjjeu
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud
[article]
2021
arXiv
pre-print
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As deep learning tasks are mostly computation-intensive, it has become a trend to process raw data on devices and send the deep neural network (DNN) features to the cloud, where the features are further ...
The framework aims to learn a policy to modify the base DNNs to prevent information leakage while maintaining high inference accuracy. ...
To achieve a better tradeoff among the objectives of privacy, accuracy and resource utilization, we present a reinforcement learning based optimizer searching for the optimal neural network transformation ...
arXiv:1912.08421v2
fatcat:um3zb6cbi5cjxmpueluw556v24
Reinforcement Learning for IoT Security: A Comprehensive Survey
2020
IEEE Internet of Things Journal
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2021 pre-print arXiv:2102.07247v1 fatcat:wyclevr3bradhfkttgwc5ji45u
In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security ...
With this paper, readers can have a more thorough understanding of IoT security attacks and countermeasures using Reinforcement Learning, as well as research trends in this area. ...
REINFORCEMENT LEARNING IN CYBER PHYSICAL SYSTEMS
A. Security in Smart Grid Smart Grid is an intelligent system to generate and distribute energy in a distributed manner. ...
doi:10.1109/jiot.2020.3040957
fatcat:qtm2emhqmjhlncyczuij6nw3oa
Learning to infer in recurrent biological networks
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.10811v1 fatcat:ix33fi66xbcxdjwrsw7sp3fibu
A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. ...
We illustrate the idea on recurrent neural networks trained to model image and video datasets. ...
Related work in generative modeling Several generative modeling papers have approached representation learning by adversarially matching joint distributions of latent vectors and inputs. ...
arXiv:2006.10811v2
fatcat:jlatutwa6vb3znm4ms4mafyrky
From internal models toward metacognitive AI
[article]
2021
arXiv
pre-print
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The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. ...
Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a "responsibility signal" that gates selection and learning of pairs in perception ...
The model comprises a modular hierarchical
reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. ...
arXiv:2109.12798v2
fatcat:7foykaimirblrcxg3anqjo2jfm
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
[article]
2020
arXiv
pre-print
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Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. ...
Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. ...
The authors also would like to thank the helpful discussions with group members of Umich Yelab and Foreseer research group. ...
arXiv:2001.06937v1
fatcat:4iqb2vnhezgjnphfv3taej7vbu
UAV Autonomous Aerial Combat Maneuver Strategy Generation with Observation Error Based on State-Adversarial Deep Deterministic Policy Gradient and Inverse Reinforcement Learning
2020
Electronics
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At the same time, a reward shaping method based on maximum entropy (MaxEnt) inverse reinforcement learning algorithm (IRL) is proposed to improve the aerial combat strategy generation algorithm's efficiency ...
Meanwhile, we propose a novel autonomous aerial combat maneuver strategy generation algorithm with high-performance and high-robustness based on state-adversarial deep deterministic policy gradient algorithm ...
Reward Shaping Using Inverse Reinforcement Learning
Reward Shaping Due to the learning efficiency of deep reinforcement learning, reinforcement learning has been restricting its practical application ...
doi:10.3390/electronics9071121
fatcat:cbu5qoteuzdovkyhhyyo2q6db4
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