Deep Reinforcement Learning Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Deep Reinforcement Learning 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.

Deep Reinforcement Learning Projects for Final Year Students

Latest Deep Reinforcement Learning Projects

  1. A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
    This project studies offline reinforcement learning, which teaches computers to make decisions using only existing data instead of interacting with the real world. It explains different methods, compares how well they work, and points out their strengths and weaknesses. The study also highlights gaps and suggests future research directions. It helps researchers understand which approaches are best for various problems in areas like healthcare, education, and robotics.
  2. Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges
    This project studies how smart transportation systems can be improved using deep learning. It looks at how traffic, vehicles, roads, and weather affect transportation. The study explains modern AI methods for predicting traffic, recognizing vehicles, and monitoring road conditions. It also highlights challenges and future ideas to make transportation safer and more efficient.
  3. Combining Lyapunov Optimization and Deep Reinforcement Learning for D2D Assisted Heterogeneous Collaborative Edge Caching
    This project focuses on improving content sharing in wireless networks. Devices can share data directly with nearby devices or get it from a base station. The method uses smart learning to decide which devices should store and share content. It reduces delays, saves energy, and keeps the network running smoothly.
  4. 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.
  5. Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
    This project focuses on making mobile networks smarter and cheaper to run. It studies how to set up and manage different parts of the network, like base stations and virtual units, depending on traffic and resources. The researchers used a type of artificial intelligence called deep reinforcement learning to find the best setup automatically. Their method reduces network costs a lot compared to older approaches.
  6. Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing
    This project focuses on helping mobile devices handle heavy computing tasks more efficiently. Instead of doing all tasks on the device, some work is sent to powerful edge servers nearby. The goal is to finish tasks faster while using less battery. The project uses smart algorithms that learn over time to make the best decisions for offloading tasks.
  7. 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.
  8. Learning Random Access Schemes for Massive Machine-Type Communication With MARL
    This project studies how multiple smart devices can share a communication network efficiently without needing complex coordination. It uses learning techniques so devices can decide when to send data on their own. The methods improve network performance and fairness while working well for many low-power devices. Simulations show the approach adapts to changing traffic and works even as more devices join the network.
  9. Multi-Agent Double Deep Q-Learning for Fairness in Multiple-Access Underlay Cognitive Radio Networks
    This project focuses on improving how wireless devices share the same communication channel without causing interference. It uses artificial intelligence to make sure all devices get a fair chance to send data. The system learns automatically to balance speed and fairness. This makes communication faster, more reliable, and efficient even when many users share the same signal space.
  10. Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies
    This project focuses on making wireless networks smarter and more secure. It uses artificial intelligence to manage network resources for many users and base stations. The system also studies how a jammer can disrupt the network and how to defend against it. The methods are tested in simulations to show they work well.
  11. Deep Reinforcement Learning Based Resource Allocation for Fault Detection with Cloud Edge Collaboration in Smart Grid
    This project builds a smart system that quickly finds faults in the power grid. It uses both cloud and edge computers to share the work of detecting problems. The edge devices do fast, light processing, while the cloud handles heavy tasks. This setup reduces delay and makes the power grid run more efficiently in real time.
  12. Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing
    This project focuses on efficiently scheduling tasks in cloud computing. It assigns complex workflows to the best virtual machines to reduce delays, energy use, and cost. The method uses a type of artificial intelligence called deep reinforcement learning to make these decisions dynamically. Tests showed it works better than existing scheduling methods.
  13. A Multiagent Meta-Based Task Offloading Strategy for Mobile-Edge Computing
    This project focuses on improving how mobile devices handle heavy computing tasks. It moves tasks from the device to nearby edge servers to save time and energy. The researchers used smart learning methods so the system can adapt to changing conditions. Their approach shows faster and more stable performance across different situations.
  14. A Spiking Reinforcement Trajectory Planning for UAV-Assisted MEC Systems
    This project focuses on reducing energy use in drones and mobile devices that work together for edge computing. It uses a new learning method that combines brain-inspired neural networks with deep learning to plan drone movements efficiently. The proposed approach trains faster and uses fewer resources than traditional methods. It helps drones make smarter and quicker decisions while saving energy.
  15. Advancing UAV Communications: A Comprehensive Survey of CuttingEdge Machine Learning Techniques
    This project studies how machine learning can help drones communicate better and work smarter in mobile networks. It explains how drones can act as flying users or base stations to improve network coverage. The paper reviews different learning methods that help drones save energy and find the best flight paths. It also explores how new AI techniques can connect with cloud and edge systems for better performance.
  16. An Efficient Multi-Edge Server Coalition Computation Offloading Scheme of Sensor-Edge-Cloud
    This project focuses on improving how wearable medical devices send and process data. It introduces a smart method that decides when and how to send heavy computing tasks to nearby mobile servers. By using multiple servers together, it reduces delay and saves energy. This makes remote medical applications like surgery faster and more reliable.
  17. An Online Simulated Annealing-Based Task Offloading Strategy for a Mobile Edge Architecture
    This project develops SATS, a method to assign tasks quickly in mobile edge computing systems. It uses a smart trial-and-error approach to schedule tasks in real time. The system works best when it slightly overestimates the number of incoming requests. This strategy improves how many tasks are accepted and reduces the time they take to process.
  18. Attention Mechanism-Aided Deep Reinforcement Learning for Dynamic Edge Caching
    This project focuses on improving mobile networks by predicting what content users will need and storing it on nearby devices before it is requested. It designs a smart system that decides what to cache and when, using a type of artificial intelligence. The goal is to reduce network traffic and make better use of limited storage and network resources. The proposed method was tested and shown to work effectively in improving speed and efficiency.
  19. DRL-Based Distributed Task Offloading Framework in Edge-Cloud Environment
    This project focuses on improving how tasks are handled in Internet of Things (IoT) systems. It combines cloud and edge computing to make task execution faster and more energy-efficient. The researchers created a smart system using deep learning to decide where tasks should run. Experiments show it saves energy, reduces delays, and works better than other methods.
  20. JDACO: Joint Data Aggregation and Computation Offloading in UAVEnabled Internet of Things for Post-Disaster Scenarios
    This project studies how drones can help IoT devices work better after disasters. The drones collect data and provide computing power to support decision-making. The researchers created a method that combines data collection and computation to save energy and reduce delays. Tests show their approach works faster, uses less energy, and serves more devices than existing methods.
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We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

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