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

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.
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.
Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

This project uses a type of machine learning called Q-learning to improve wireless sensor networks. It focuses on choosing special nodes, called cluster heads, to collect and send data efficiently. The method reduces energy use and shortens the travel path of a mobile base station. Simulations show it performs better than traditional approaches in saving energy and extending network life.
Secure Relay Selection with Outdated CSI in Cooperative Wireless Vehicular Networks A DQN Approach

This project focuses on improving wireless communication between vehicles. It develops smart methods to choose the best relay car for sending data, even when the network information is outdated. The system uses advanced machine learning techniques to reduce the chances of data being intercepted. Simulations show that the proposed methods perform much better than traditional approaches.

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Reinforcement Learning Algorithm Final Year Projects with Source Code