Deep Reinforcement Learning and Its Latest Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3804

Special Issue Editor


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Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: deep learning; generative models; diffusion models; deep reinforcement learning

Special Issue Information

Dear Colleagues,

Despite a few remarkable achievements, reinforcement learning is a field within artificial intelligence that has seen a relatively limited impact of deep learning techniques thus far. While neural networks have helped overcome some scalability issues associated with traditional methods, the fundamental methodologies have remained largely unchanged, leaving many traditional problems unresolved. There are several challenges that still need to be addressed or better understood in RL: sample efficiency and moving beyond the current “tabula rasa” approach, the exploitation vs. exploration dilemma, the lack of generalization and difficult adaptation to different scenarios, intrinsic vs. extrinsic rewarding systems, and intelligent transfer learning, among many others.

There is a widely held belief that solving the aforementioned challenges, which are interconnected to some extent, requires a significant paradigm shift in the field of RL. This shift is likely to be centered around a more comprehensive and extensive utilization of deep learning techniques. In light of this perspective, we strongly encourage the submission of innovative and visionary works that align with this vision. We welcome contributions that present mature applications addressing tangible problems, as well as well-crafted proof-of-concept articles that showcase and promote pioneering approaches.

Prof. Dr. Andrea Asperti
Guest Editor

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Keywords

  • sample efficiency
  • multi-agent DRL
  • transfer learning
  • generalization
  • intrinsic rewarding systems
  • hierarchical DRL
  • sparse reward environments
  • multi-objective RL
  • partial observability
  • multi-task and meta RL

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Published Papers (3 papers)

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15 pages, 6053 KiB  
Article
Designing Spiking Neural Network-Based Reinforcement Learning for 3D Robotic Arm Applications
by Yuntae Park, Jiwoon Lee, Donggyu Sim, Youngho Cho and Cheolsoo Park
Electronics 2025, 14(3), 578; https://doi.org/10.3390/electronics14030578 - 31 Jan 2025
Viewed by 287
Abstract
This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking neural networks-based twin delayed deep deterministic policy gradient in [...] Read more.
This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking neural networks-based twin delayed deep deterministic policy gradient in 3D robotic manipulation, demonstrating its extension from traditional 2D tasks to complex 3D target-reaching scenarios with improved energy efficiency and stability. Additionally, with the inertial measurement unit data the system successfully mimics human arm movements, achieving a success rate of 0.95 among 50 trials and enabling an intuitive and accurate human–robot interaction system. This pioneering attempt highlights the feasibility of combining the biologically inspired spiking neural networks with the reinforcement learning algorithm to address the real-time challenges in high-dimensional robotic environments and advance the field of human–robot interaction systems. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning and Its Latest Applications)
12 pages, 1357 KiB  
Article
Energy Efficient Power Allocation in Massive MIMO Based on Parameterized Deep DQN
by Shruti Sharma and Wonsik Yoon
Electronics 2023, 12(21), 4517; https://doi.org/10.3390/electronics12214517 - 2 Nov 2023
Cited by 3 | Viewed by 1525
Abstract
Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user [...] Read more.
Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2% and 108.5% improvement over DQN methods and the RL method, respectively, in handling large-scale multi-agent problems in M-MIMO networks. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning and Its Latest Applications)
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14 pages, 1305 KiB  
Systematic Review
Role of Virtual Reality in Improving Home Cancer Care: A Systematic Literature Review
by Safa Elkefi and Avishek Choudhury
Electronics 2025, 14(2), 385; https://doi.org/10.3390/electronics14020385 - 20 Jan 2025
Viewed by 661
Abstract
Virtual reality (VR) can play an important role in supporting remote care for cancer patients. The purpose of this study is to explore the possible applications of VR in-home cancer care to support patients and healthcare practitioners. We conducted a systematic literature search [...] Read more.
Virtual reality (VR) can play an important role in supporting remote care for cancer patients. The purpose of this study is to explore the possible applications of VR in-home cancer care to support patients and healthcare practitioners. We conducted a systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched for peer-reviewed publications in PubMed, Web of Science, and IEEE Xplore for research articles published in the last two decades. After the final list of relevant articles was identified, we adopted an inductive approach to categorize and report our findings. We identified 15 relevant research articles and categorized them into three themes: medical treatment, emotional support, and education and training. Six articles leveraged VR to support medical treatment such as outpatient physical therapy and rehabilitation, and pain management. Five used VR to provide emotional support to patients by uplifting their feelings or providing psychological guidance. Lastly, four leveraged VR for education and training purposes. Overall, all studies reported positive outcomes of VR for home cancer care. Our review advocates for VR integration in in-home cancer care. The findings of this review acknowledge the potential of VR in augmenting medical treatment, providing emotional support to patients, and facilitating the education and training of patients and clinicians. VR in-home cancer care can provide patients with a better quality of care. However, just like any other technological integration, VR can also introduce different challenges, including usability, affordability, and acceptance. Therefore, more studies evaluating VR’s usability and acceptance should be conducted. Additionally, stakeholders such as regulatory bodies, patients, and payors should be involved in ensuring VR’s affordability and developing protocols for its safe use. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning and Its Latest Applications)
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