Policy Gradient with PyTorch

Hugging Face's Deep Reinforcement Learning course has spent four units drilling value-based methods, culminating in Deep Q-Learning. Unit 5 flips the script. Rather than estimating Q-values and picking the best action greedily, policy-gradient methods tune a policy's parameters directly using gradient ascent, chasing higher expected returns without ever building a value function as a middleman.

The featured algorithm here is Reinforce, also known as Monte Carlo Policy Gradient. It runs an episode start to finish, tallies up the return, then nudges the policy's weights so that action-state pairs from a high-scoring episode become more likely next time, and pairs from a low-scoring episode become less likely. No lookup tables, no separate value network, just a probability distribution over actions that shifts in response to how well things actually turned out.

Why bother when Deep Q-Learning already works? The course lays out concrete wins for policy gradients: they naturally output stochastic policies, so you skip hand-tuning an exploration-exploitation schedule, and they dodge perceptual aliasing, the annoying situation where a deterministic agent sees two identical-looking states that actually require opposite actions (the example given is a vacuum robot that gets stuck between two walls it can't visually distinguish). Policy gradients also scale to continuous or huge action spaces, like a self-driving car choosing a steering angle from a near-infinite range, where computing a Q-value for every possible action becomes its own optimization headache.

None of this is free, though. Reinforce tends to settle into local maxima rather than a global optimum, learns slower because it updates step by step, and suffers from high variance in its gradient estimates, a problem the course flags as solvable with a baseline technique down the line. The tutorial has learners implement Reinforce from scratch in PyTorch and stress-test it across CartPole-v1, the trickier PixelCopter, and Pong, then upload results to a shared leaderboard.

Unit 5 caps off a stretch the instructors admit is genuinely disorienting, and they're upfront that confusion here is normal even for people who've been doing RL for years. The next unit moves toward Actor-Critic methods, which blend policy-based and value-based approaches, suggesting Reinforce is less an endpoint than a stepping stone toward hybrids that fix its variance problem while keeping its flexibility.