Q-learning Path Planning Algorithm Implementation

This project implements the Q-learning path planning algorithm from reinforcement learning using MATLAB programming. Q-learning represents a classical reinforcement learning algorithm designed to solve path planning problems through value iteration methodology. The algorithm operates by learning and updating state-action value functions (Q-values) to determine optimal action selection policies. Key implementation aspects include: - Initializing Q-table with zeros or small random values for exploration - Implementing the Q-value update rule: Q(s,a) = Q(s,a) + α[r + γmaxQ(s',a') - Q(s,a)] - Designing reward functions for path planning scenarios (positive rewards for goal states, negative for obstacles) - Configuring exploration-exploitation balance using ε-greedy policy - Setting learning rate (α) and discount factor (γ) parameters appropriately MATLAB provides a powerful programming environment with comprehensive toolboxes and function libraries that facilitate efficient implementation of Q-learning algorithms. The implementation leverages MATLAB's matrix operations for efficient Q-table updates and visualization capabilities for path analysis. Through this project, developers gain deep understanding of reinforcement learning principles and path planning applications while acquiring practical programming and experimental experience in algorithm tuning and performance evaluation. The code implementation enables identification of optimal path selections, thereby enhancing system efficiency and performance in autonomous navigation scenarios.