Evaluating Reinforcement Learning Algorithms for LunarLander-v2: A Comparative Analysis of DQN, DDQN, DDPG, and PPO

This study investigates the performance and optimization strategies of advanced reinforcement learning algorithms: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) to solve the LunarLander-v2 control task. Each algorithm is analyzed on the basis of its design principles, training stability, and adaptability to complex action and state spaces. DDQN addresses overestimation biases in DQN by leveraging dual networks for more accurate value estimation. DDPG incorporates an actor-critic architecture to excel in continuous control tasks, while PPO introduces a policy gradient approach with clipped updates to balance exploration and exploitation. Hyperparameter tuning, including learning rates, discount factors, and batch sizes, is explored to optimize the performance of each method. The results demonstrate significant improvements in stability, learning efficiency, and final scores as training progresses. Comparative insights highlight the trade-offs between sample efficiency, stability, and computational overhead across these algorithms, providing valuable guidance for reinforcement learning applications in dynamic control environments.