Yazed Alsaawy

Shaping Gradient and Exploration-Noise Initialization, Not Reward Polarity, Determine Convergence in Deep Reinforcement Learning for Autonomous Quadrotor Navigation and Obstacle Avoidance

This paper presents a systematic reward engineering methodology for training a Proximal Policy Optimization (PPO) quadrotor navigation policy in the Webots simulator, using a hierarchical architecture in which a PID controller handles low-level stabilization and a PPO policy issues velocity commands. We document the co...