Machine Learning: An Introduction to Reinforcement Learning

Fayaz, Sheikh Amir; Sidiq, Sumina; Zaman, Majid; Butt, Muheet Ahmed

doi:10.1002/9781119776499.ch1

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…However, Reinforcement Learning can learn an optimal coping strategy in a dynamic environment [32]. The policy enables the agent to adaptively take actions that gain the largest cumulative reward at the current environmental state [33]. Different from Reinforcement Learning, DRL uses a deep neural network to approximate the action-value function.…”

Section: Drl and Its Applications In Mopsmentioning

confidence: 99%

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Ming

Gong

Gao

2024

IEEE Trans. Evol. Computat.

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Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

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Section: Drl and Its Applications In Mopsmentioning

confidence: 99%

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Ming

Gong

Gao

2024

IEEE Trans. Evol. Computat.

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“…The Image Net Big Scale Visual Recognition Challenge contributed significantly to the advancement of convolution neural networks. The successful designs in this contest represent the cutting edge of deep learning and neural networks [25]. They provide a springboard for creative thought and a foundation for reconsidering existing problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Neural Network-Powered, Driverless Vehicle Concept Development

Kadeem,

Naser,

Hassan

et al. 2023

AKJS

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Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.

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“…This learning technique is iterative, with the agent continually refining its actions based on past experiences to better future outcomes. It is a versatile tool for solving complex issues [7]. In recent years, RL research has been focusing on applying its concepts to real-world problems.…”

Section: Wireless Networkmentioning

confidence: 99%

DQN-GNN-Based User Association Approach for Wireless Networks

Alablani,

Alenazi

2023

Mathematics

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In the realm of advanced mobile networks, such as the fifth generation (5G) and beyond, the increasing complexity and proliferation of devices and unique applications present a substantial challenge for User Association (UA) in wireless systems. The problem of UA in wireless networks is multifaceted and requires comprehensive exploration. This paper presents a pioneering approach to the issue, integrating a Deep Q-Network (DQN) with a Graph Neural Network (GNN) to enhance user-base station association in wireless networks. This novel approach surpasses recent methodologies, including Q-learning and max average techniques, in terms of average rewards, returns, and success rate. This superiority is attributed to its capacity to encapsulate intricate relationships and spatial dependencies among users and base stations in wireless systems. The proposed methodology achieves a success rate of 95.2%, outperforming other methodologies by a margin of up to 5.9%.

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Machine Learning: An Introduction to Reinforcement Learning

Cited by 10 publications

References 27 publications

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Artificial Neural Network-Powered, Driverless Vehicle Concept Development

DQN-GNN-Based User Association Approach for Wireless Networks

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