Most Beginners Pick the Wrong RL Framework
You don't need RLlib. I'll say it again: if you're starting reinforcement learning from scratch, RLlib is overkill that will slow you down for at least your first three projects.
The RL framework landscape looks intimidating because everyone conflates three separate concerns: environment APIs (Gymnasium), algorithm libraries (Stable Baselines3), and distributed training platforms (RLlib). Beginners see "production-grade" and "scalable" in RLlib's docs and assume that's what serious ML engineers use. But here's what actually happens: you spend two days debugging Ray cluster configs, another day figuring out why your custom callback isn't firing, and you still haven't trained a single agent.
I'm going to walk through the actual API complexity, setup time, and cognitive overhead of each framework using a concrete example—training PPO on a custom environment. By the end, you'll know exactly which tool matches your current skill level and project scope.
Continue reading the full article on TildAlice
Top comments (0)