Technical Reports
TR001 PCT Applied to Lunar Lander with Comparative RL Baseline TR001_v1.0.12.pdf
This research investigates the comparative performance of Perceptual Control Theory (PCT) and Reinforcement Learning (RL) within the Lunar Lander simulation environment, aiming to address the research gap regarding the efficacy of PCT as a control strategy for autonomous agents.
Key findings reveal that the PCT controller achieved a higher success rate of 79% compared to the RL’s 75%, despite utilising significantly fewer parameters by a factor of 10,000 (29 versus 335,622). This result underscores PCT’s computational efficiency and robustness, making it a viable alternative in scenarios where resource constraints and system transparency are critical.