Reinforcement-Learning

value-based methods (q-learning and family) learn how good actions are and act by argmax. policy-gradient methods skip the middleman: parameterise the policy itself, \(\pi_\theta(a \mid s)\), and do gradient ascent on expected return. 𐃏 the entire family — reinforce, actor-critic, trpo, ppo, and by extension rlhf — rests on one identity, the policy gradient theorem, whose derivation is three lines of calculus and one very good idea. the standard reference is sutton & barto, free at http://incompleteideas.net/book/the-book-2nd.html.

q-learning is the algorithm that made reinforcement learning feel inevitable: interact with an unknown world, nudge a table of numbers after every step, and the table converges to the value of optimal behaviour — even while you behave suboptimally the entire time. 𐃏 everything runs on one line of arithmetic, and the rest of this page is the machinery needed to say precisely why that line works. the canonical reference for all of it is sutton & barto’s reinforcement learning: an introduction, free at http://incompleteideas.net/book/the-book-2nd.html.