Implemented Q-learning and Value Iteration agents for policy optimization and exploration.
Implemented Value Iteration and Q-learning agents for policy optimization tasks. Experimented with discount factors, noise, and living rewards to analyze agent behavior in environments such as Pacman and bridge crossing. Developed Epsilon-Greedy exploration and Approximate Q-learning for scalability.
An evidence-based AI system using RAG to generate ATS-optimized resumes from GitHub and research dat...
AI-based gait analysis using deep learning and signal processing for motion understanding....
Character-level music generation using LSTM-based RNNs trained on symbolic music data....