A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Zai, Wenjiao; Wang, Junjie; Li, Guohui

doi:10.3390/su151713127

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…This static approach fails to incorporate a built-in mechanism to favor the selection of an arm that has remained unchosen for an extended period. This oversight in the exploration strategy potentially undermines the algorithm's efficiency and effectiveness in diverse or evolving scenarios [2]…”

Section: 21asymptotically Optimal Ucbmentioning

confidence: 99%

Advancing Frontiers: An In-Depth Analysis of Reinforcement Learning Applications in Recommendation System Technologies

Zhu

2024

HSET

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This article commences by elucidating the concept and algorithms underpinning reinforcement learning (RL), laying the foundation for a comprehensive understanding of RL's principles. It then draws a detailed comparison between RL and traditional machine learning paradigms, using illustrative examples to highlight the distinctive methodologies and outcomes of these approaches. This comparison not only clarifies the unique attributes of RL but also contextualizes its position within the broader landscape of machine learning techniques. Subsequently, the focus shifts to recommendation systems (RS), where both the conceptual framework and algorithmic foundations are thoroughly examined. This exploration is not limited to the technical mechanics of RS but extends to an appraisal of their diverse applications, showcasing how these systems have become integral in various domains, from e-commerce to content curation. The core of the discussion then converges on the application of RL within the realm of Chinese RS. This section delves into how RL's dynamic learning capabilities and adaptability enhance the functionality and effectiveness of RS, particularly in the context of the unique market dynamics and user behaviors observed in China. The synergy between RL and RS in this context is dissected, offering insights into how RL-driven RS can lead to more personalized, context-aware, and efficient user experiences.

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Section: 21asymptotically Optimal Ucbmentioning

confidence: 99%

Advancing Frontiers: An In-Depth Analysis of Reinforcement Learning Applications in Recommendation System Technologies

Zhu

2024

HSET

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“…They are commonly referred to as RL method for MDP (RLMDP) [14,15]. Tasks with Markovian properties, such as scheduling unmanned drones for emergency power supply transport or formulating distribution strategies for warehouse locations in the supply chain, can all be addressed using the RLMDP method [16,17]. RLMDP primarily relies on learning from empirical knowledge, allowing decision makers to adapt their strategies over time to maximize their payoffs [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error

Wang,

Yang,

Chen

et al. 2023

Electronics

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Traditional backward recursion methods face a fundamental challenge in solving Markov Decision Processes (MDP), where there exists a contradiction between the need for knowledge of optimal expected payoffs and the inability to acquire such knowledge during the decision-making process. To address this challenge and strike a reasonable balance between exploration and exploitation in the decision process, this paper proposes a novel model known as Temporal Error-based Adaptive Exploration (TEAE). Leveraging reinforcement learning techniques, TEAE overcomes the limitations of traditional MDP solving methods. TEAE exhibits dynamic adjustment of exploration probabilities based on the agent’s performance, on the one hand. On the other hand, TEAE approximates the optimal expected payoff function for subprocesses after specific states and times by integrating deep convolutional neural networks to minimize the temporal difference error between the dual networks. Furthermore, the paper extends TEAE to DQN-PER and DDQN-PER methods, resulting in DQN-PER-TEAE and DDQN-PER-TEAE variants, which not only demonstrate the generality and compatibility of the TEAE model with existing reinforcement learning techniques but also validate the practicality and applicability of the proposed approach in a broader MDP reinforcement learning context. To further validate the effectiveness of TEAE, the paper conducts a comprehensive evaluation using multiple metrics, compares its performance with other MDP reinforcement learning methods, and conducts case studies. Ultimately, simulation results and case analyses consistently indicate that TEAE exhibits higher efficiency, highlighting its potential in driving advancements in the field.

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Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Bhuiyan,

Walker,

Roni

et al. 2024

Expert Systems with Applications

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A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Cited by 4 publications

References 30 publications

Advancing Frontiers: An In-Depth Analysis of Reinforcement Learning Applications in Recommendation System Technologies

Advancing Frontiers: An In-Depth Analysis of Reinforcement Learning Applications in Recommendation System Technologies

A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

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