Policy Gradient Implementation in POMDP with MATLAB Code

I provide here a more detailed explanation to help you better understand policy gradients in POMDPs and their MATLAB implementation. Policy gradient is a reinforcement learning approach that enables agents to learn reward-maximizing behaviors without requiring explicit knowledge of the environment model. POMDP (Partially Observable Markov Decision Process) represents a widely-used reinforcement learning framework that incorporates partial observability and stochasticity, making it more challenging than fully observable models. The MATLAB implementation is particularly valuable as it offers a convenient platform for simulation and testing to deepen understanding of this complex concept. The code includes detailed annotations about policy gradient methods, accompanied by practical examples demonstrating their application to solve POMDP problems. Key implementation aspects covered include gradient estimation techniques, policy parameter updates using stochastic gradient ascent, and belief state management for handling partial observability. The code structure organizes core functions such as policy evaluation, gradient computation, and trajectory sampling, with special attention to efficiency considerations for large state spaces. This resource aims to provide comprehensive insights into both theoretical foundations and practical implementation details of policy gradients in POMDP environments.