Deep Q-Network Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Deep Q-Network ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Deep Q-Network Projects
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ACERAC: Efficient Reinforcement Learning in Fine Time Discretization
This project focuses on teaching machines to learn the best actions on their own instead of being told what to do. It improves how machines try different actions over time so they learn smoothly without sudden jerks. The researchers created a new learning method that remembers past actions and uses them to make better decisions. Tests in simulation showed this method works better than other popular approaches in most cases. -
Deep Learning for Radio Resource Allocation Under DoS Attack
This project develops an intelligent system that helps wireless networks stay secure and efficient even under cyberattacks. It uses deep reinforcement learning to manage how sensors send data and save energy while resisting denial-of-service attacks. The system can also detect when attackers change their strategy and quickly adapt to it. This makes the network more reliable and resilient in real-time conditions. -
Fast Context Adaptation in Cost-Aware Continual Learning
This project studies how smart computer programs can manage resources in 5G networks. It looks at a problem where learning these strategies can use up the network’s resources and affect users. The researchers propose a method that lets the program learn quickly while using very few resources. Their approach adapts to changes and keeps the user experience smooth. -
Hierarchical Reinforcement Learning for Multi-Layer Multi-Service NonTerrestrial Vehicular Edge Computing
This project focuses on improving computing for smart vehicles. It combines ground-based and satellite edge computing to help vehicles process data faster and in more areas. The system uses machine learning to decide where and how much data to process for lower delay and energy use. Simulations show that this approach works better than existing methods. -
Advanced Control Strategies for Photovoltaic Power Quality and Maximum Power Point Tracking Optimization
This project improves how solar power systems send clean and stable electricity to the grid. It uses a smart control method to reduce unwanted electrical noise and keep the power steady even when grid conditions change. The system also adjusts itself to get the maximum power from the solar panels. Overall, it makes the solar setup more reliable, efficient, and environmentally friendly.
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