Reinforcement Learning Final Year Projects with Source Code

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Reinforcement Learning Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These 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.

Reinforcement Learning Final Year Projects

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. A Hybrid Proactive Caching System in Vehicular Networks Based on Contextual Multi-Armed Bandit Learning
    This project predicts which roadside unit a moving vehicle will connect to next. By knowing this early, the network can store the needed data in advance and reduce delay for users. The system uses learning methods that allow each roadside unit to make its own predictions. Tests in different cities show that the method predicts vehicle movement with high accuracy, even in complex traffic conditions.
  7. 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.
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