Reinforcement Learning in Practice

Reinforcement learning is less about elegant equations and more about plumbing: fast simulators, stable losses, honest evaluation, and guardrails against reward hacking. In Python, that means Gymnasium/envpool for environments, a PyTorch loop (or a focused library) for agents, and careful data handling when you can’t act online. We’ll also demystify RLHF/RLAIF – how preference data turns “don’t do that” into gradients – and close with a pragmatic tour of PyTorch-first RL libraries.