Show HN: Fast Deep Reinforcement Learning Course
To be honest though, the practical side of things of RL can be a hit-and-miss in terms of &quot;fun&quot; depending on the person. It requires a lot of manual hand tuning, reward shaping, hyperparameter tuning, and general trial-and-error to make an agent do a seemingly simple-enough task, and these tricks are more heuristically and haphazardly done than what you would expect from more &quot;conventional&quot; programming. It is fun for the right people (who loves tinkering with stuff and also have the perseverance to continually run RL experiments that can last hours or even days). But I would imagine many getting bored by the whole experience. (Pssst.... I was one of them, switched to doing something else in the middle of grad school)<p>By the way, RLlib is good if you want to try out simple experiments with well-established RL algorithms, but it&#x27;s <i>really</i> awful to use when you want to modify the algorithm even just a little bit. So it&#x27;s not bad for beginner-level tutorials, but once you get the basics it might be very frustrating later on. I would recommend simpler frameworks like Stable Baselines 3 (<a href="https:&#x2F;&#x2F;stable-baselines3.readthedocs.io&#x2F;en&#x2F;master&#x2F;" rel="nofollow">https:&#x2F;&#x2F;stable-baselines3.readthedocs.io&#x2F;en&#x2F;master&#x2F;</a> ) for a much more stable experience, if you have gained a fair bit of Python&#x2F;ML programming skills at hand and don&#x27;t have trouble reading well-maintained library code.