Policy Gradient Strategies for Lunar Lander Navigation: A Deep Reinforcement Learning Approach (REINFORCE).

This research investigates Policy Gradient strategies, specifically the REINFORCE algorithm with various baseline improvements, for solving the Lunar Lander problem in OpenAI Gym.We demonstrate how REINFORCE directly optimizes policy parameters without maintaining Q-values, making it particularly suitable for continuous state spaces where traditional tabular methods fail.Our comparative analysis reveals that while basic REINFORCE achieves moderate success, incorporating adaptive baselines significantly reduces gradient variance and improves learning stability.The implementation achieves optimal landing performance by balancing exploration and exploitation through Monte-Carlo methods, with visual confirmation of successful landings between designated flags.