Policy Gradients: REINFORCE from Scratch with NumPy

In the DQN post, we trained a neural network to estimate Q-values and then picked the best action with argmax. That works when the action space is discrete — push left or push right. But what if you need to control a robotic arm with continuous joint angles, or steer a car with a continuous throttle? You can't argmax over infinity. Policy gradient methods flip the approach: instead of learning a value function and deriving a policy, we directly parameterise the policy and optimise it via gradie...