Build Superhuman Reinforcement Learning in 10 Minutes

The motivation here was to expose most people who probably haven't heard of RL concepts like rewards, Markov decision processes, etc to the ideas. Appreciate your comment and I understand that a naive search method might be basic for advanced practitioners such as yourself :)

Nothing prevents you from rewarding 0 for anything other than a win. A win would be +1 and a loss would be -1 and every intermediate step is 0. However, this yields the new problem of your reinforcement learner needs to explore all paths to all possible outcome - the exact thing we are trying to get around. Thus, we can use human heuristics or neural networks to try and learn these heuristics. The latter approach is deep reinforcement learning and the basis for Alpha Zero.

Thanks for the comment. 100% it is a naive random policy search. The intention is to help those who might not be familiar with RL concepts understand basic tools & how to start playing around rather than diving into things like DQL, PPO, etc right away

Have you solved the lunar lander environment? It's been kicking my ass for a few months off and on. I finally solved it with vanilla policy gradiant ascent without a baseline, it just took 30,000 episodes.

Does what you've created in the blog post solve the lunar lander?