Image Classification by Reinforcement Learning With Two‐State Q‐Learning

Hafiz, Abdul Mueed

doi:10.1002/9781119792642.ch9

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Different approaches and variations of these algorithms have been proposed and are used in a wide spectrum of applications such as simulating non-player character (NPC) behavior in Racing Games, image classification, training of mobile robots, and AI for simple board games…”

Section: Methodsmentioning

confidence: 99%

Reinforcement Learning Approaches for the Optimization of the Partial Oxidation Reaction of Methane

Neumann

2022

Ind. Eng. Chem. Res.

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Optimizing reactions in the chemical industry is one of the major challenges in the pursuit of economic and ecological sustainability. With ongoing research in this field, the amount of available data has greatly increased, which makes it suitable for machine learning approaches. In this paper, the application of reinforcement learning for finding optimal reaction conditions of the partial oxidation of methane (POX) is tested. Q-learning (QL) agents and deep deterministic policy gradient (DDPG) agents are trained to maximize H2 production by partial oxidation of methane in a simulated plug flow reactor. Although the QL agent showed promising results in a simplified environment, it was not able to achieve improvements in the simulation environment. A clear superiority of the DDPG agent was observed, as it was able to maximize H2 production by adjusting temperature, pressure, flow velocity, and substrate composition. This proves that reinforcement learning is applicable for reaction optimization and a promising concept to improve efficiency in chemical processes.

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Section: Methodsmentioning

confidence: 99%

Reinforcement Learning Approaches for the Optimization of the Partial Oxidation Reaction of Methane

Neumann

2022

Ind. Eng. Chem. Res.

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“…Chen et al [34] proposed a recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions. Hafiz et al [35] used a Q-learning with an agent having two states and two to three actions to achieve an efficient and simple image recognition classification system. Li et al [36] proposed a novel framework based on reinforcement learning for pre-selecting useful images for emotion classification.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Information Fusion Based on Multi-View and Multi-Feature Learning

Li¹,

Zhang²,

Zhang³

2022

Information Fusion

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With the development of deep learning techniques, supervised learning has achieved performances surpassing those of humans. Researchers have designed numerous corresponding models for different data modalities, achieving excellent results in supervised tasks. However, with the exponential increase of data in multiple fields, the recognition and classification of unlabeled data have gradually become a hot topic. In this paper, we employed a Reinforcement Learning framework to simulate the cognitive processes of humans for effectively addressing novel class discovery in the Open-set domain. We deployed a Member-to-Leader Multi-Agent framework to extract and fuse features from multi-modal information, aiming to acquire a more comprehensive understanding of the feature space. Furthermore, this approach facilitated the incorporation of self-supervised learning to enhance model training. We employed a clustering method with varying constraint conditions, ranging from strict to loose, allowing for the generation of dependable labels for a subset of unlabeled data during the training phase. This iterative process is similar to human exploratory learning of unknown data. These mechanisms collectively update the network parameters based on rewards received from environmental feedback. This process enables effective control over the extent of exploration learning, ensuring the accuracy of learning in unknown data categories. We demonstrate the performance of our approach in both the 3D and 2D domains by employing the OS-MN40, OS-MN40-Miss, and Cifar10 datasets. Our approach achieves competitive competitive results. The source code can be found here: https://github.com/AveryFallson/RMFFN

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A Survey of Deep Q-Networks used for Reinforcement Learning: State of the Art

Hafiz

2022

Lecture Notes on Data Engineering and Communications Technologies

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Image Classification by Reinforcement Learning With Two‐State Q‐Learning

Cited by 7 publications

References 32 publications

Reinforcement Learning Approaches for the Optimization of the Partial Oxidation Reaction of Methane

Reinforcement Learning Approaches for the Optimization of the Partial Oxidation Reaction of Methane

Information Fusion Based on Multi-View and Multi-Feature Learning

A Survey of Deep Q-Networks used for Reinforcement Learning: State of the Art

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