One Lander, Three Controllers: Reinforcement Learning, MPC, and Quadratic Programming
I was always interested in the Lunar Lander game and suddenly I heard about model predictive control and quadratic programming. I want to see if the Lunar Lander RL can be adapted to that one in here. That question led me to build three related experiments around Gymnasium's LunarLander-v3 : Lunar RL : a causal-transformer PPO policy trained through reinforcement learning. Lunar MPC : adaptive model predictive control using discrete beam search. Lunar QP : model predictive control whose plan is solved as a convex quadratic program. All three control the same lander, but they answer very different questions. RL asks, “Can experience produce a policy that maps observations directly to actions?” MPC asks, “Given an approximate physics model, which short action sequence looks best from the current state?” QP asks, “Can that short-horizon plan be expressed as a fast convex optimization problem with explicit constraints?” The progression is not a conv...