Deep Reinforcement Learning Final Year Projects with Source Code

Byadmin Final Year Projects

Deep Reinforcement Learning Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Deep Reinforcement Learning projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Deep Reinforcement Learning Final Year Projects

  1. Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification
    This project focuses on improving brain tumor detection from MRI images using artificial intelligence. The researchers developed a deep learning model that automatically processes and classifies images, reducing errors and saving time. They used special filters and mathematical techniques to extract important features from the images. The model achieved very high accuracy of 98.8% when tested on brain tumor data.
  2. Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Industrial IoT in MEC Federation System
    This study focuses on improving smart industrial systems that use IoT devices. These devices have limited power and memory, so the project uses mobile edge computing to help them process data more efficiently. The researchers developed a smart method using deep learning to decide how tasks are shared and resources are used across devices. Their approach reduces energy use and processing delays in real-world scenarios.
  3. A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain
    This project creates a smart platform for sharing computing tasks in a secure and efficient way. It uses blockchain to make transactions safe and transparent. The system predicts how long tasks will take using deep learning. Users can choose faster results or lower costs, letting slower computers still compete by offering cheaper prices.
  4. A Mean-VaR Based Deep Reinforcement Learning Framework for Practical Algorithmic Trading
    This project focuses on creating a smart system to help investors manage stock portfolios. It uses past market data to suggest which stocks to buy or sell. The system can handle market ups and downs and learns to improve its decisions over time. Tests show it performs better than standard strategies.
  5. Boosting Performance of Visual Servoing Using Deep Reinforcement Learning From Multiple Demonstrations
    This project uses multiple expert controllers with deep reinforcement learning to improve a robot’s visual servoing, which is how a robot moves based on camera images. It creates safe action limits from expert demonstrations to reduce wasted trial-and-error learning. This approach makes training faster and improves performance in real-world scenarios. Compared to traditional methods, it achieves better accuracy and control while cutting training time almost in half.
  6. Deep Multi-Agent Reinforcement Learning With Minimal Cross-Agent Communication for SFC Partitioning
    This project focuses on improving how network services are organized and managed using virtual systems instead of physical devices. It introduces a smart system where multiple agents learn together to efficiently assign tasks in a network. The approach allows these agents to communicate and cooperate, leading to better performance than traditional centralized methods. Simulations show that the method works well across different network setups.
  7. DeepMist Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data
    This project focuses on predicting heart disease using advanced computing techniques. It proposes a system called DeepMist that uses deep learning on a Mist computing setup to analyze healthcare data quickly and efficiently. The model predicts heart disease with high accuracy while reducing energy use and processing delay. Testing shows it performs better than other popular machine learning methods.
  8. Research on ATO Control Method for Urban Rail Based on Deep Reinforcement Learning
    This project develops an intelligent system to control urban trains automatically. It uses a deep learning algorithm to adjust the train’s speed and stops in real time. The system improves punctuality, parking accuracy, and saves energy. Tests on a Beijing subway line show it works better than traditional control methods.
  9. A DRL-Based Automated Algorithm Selection Framework for Cross-Layer QoS-Aware Scheduling and Antenna Allocation in Massive MIMO Systems
    This project improves how mobile networks manage many antennas and users at the same time. It uses a learning model that picks the best scheduling and antenna methods based on current network traffic. The system learns from experience and adjusts its choices to keep users satisfied. Tests show it serves more users compared to fixed or traditional methods.
  10. CDEIR Intelligent Routing for Efficient Wireless Sensor Networks Using BUG Algorithm
    This project focuses on improving data transmission in Wireless Sensor Networks. It proposes a new routing method that avoids congested nodes, reduces delays, and saves energy. The system finds better paths for data to travel to the main Base Station. Simulations show that this approach works efficiently and quickly.
  11. Deep Reinforcement Learning for the Co-Optimization of Vehicular Flow Direction Design and Signal Control Policy for a Road Network
    This project uses a smart learning method to improve traffic flow in a city. It not only controls traffic signals but also optimizes the directions vehicles can take on roads. The system tests different road directions and signal plans, keeping the best ones to reduce congestion. Results show it improves traffic movement and reduces waiting times compared to traditional methods.
  12. Location Optimization Based on Improved 3D DV-HOP Algorithm in Wireless Sensor Networks
    This project improves how wireless sensor networks locate their nodes in complex 3D environments. It modifies an existing algorithm to reduce errors and increase accuracy. The method uses multiple communication ranges and a particle swarm optimization technique. Experiments show it works fast and precisely, achieving about 96% accuracy.
  13. Machine Learning Empowered Emerging Wireless Networks in 6G Recent Advancements Challenges and Future Trends
    This project explains how future 6G networks will use machine learning to make communication faster, smarter, and more reliable. It shows how ML helps the network manage resources on its own and solve problems like energy use, delays, and smooth connections. The paper reviews different ML methods and how they improve new types of networks, such as device-to-device and vehicular systems. It also discusses open challenges and future research directions for building intelligent 6G networks.
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