Lunar lander is a one of the problems in Open AI Gym, where you test AI against standard set of problems: https://www.gymlibrary.dev/environments/box2d/lunar_lander/ Then you have stable baselines which implements popular reinforcement learning algorithms to solve these gym problems: https://stable-baselines3.readthedocs.io/en/master/ Shamless plug: I've built a series of games where you solve puzzles (2048) / toy pr…
Yes, I would like to see the environment ported to Python, wrapped in gym, and given a good shaped reward, i.e. like reward = prior_height_delta - (height - target_height) - fuel_cost. Run Stable Baselines PPO or DQN on that and it should converge to something close to an optimized MPC controller.
You can run PPO or DQN right now on the Open AI Gym implementation using Stable-Baselines3: https://stable-baselines3.readthedocs.io/en/master/
In fact I previously ran it locally and PPO solved the problem within 10 minutes of training with max reward of about 200.