What is the REINFORCE algorithm?

REINFORCE is a policy gradient method that samples actions from a parameterized policy (like a neural net), computes log-prob gradients, weights by discounted rewards, and updates via ascent. No value function needed.

How to implement policy gradients from scratch in NumPy?

Use sigmoid for binary actions, ReLU hidden, manual backprop with chain rule, RMSProp for updates. Discount rewards backward, standardize, multiply into dlogps. Full code ~100 lines for CartPole.

Does REINFORCE work on CartPole?

Absolutely—hits 500-step max avg with tuned gamma=0.99, batch=5. High variance, but baselines fix that; pure version shines on simple envs.

🤖 AI Dev Tools

REINFORCE in 100 Lines of NumPy: Why Frameworks Might Be Overkill for Policy Gradients

What if the secret to mastering reinforcement learning isn't buried in PyTorch's layers, but in 100 lines of raw NumPy? This scratch-built REINFORCE nails CartPole—framework-free.

theAIcatchup Apr 08, 2026 4 min read

Rolling average plot of REINFORCE training on CartPole-v1, converging to 500 steps in NumPy

⚡ Key Takeaways

REINFORCE nails CartPole in 100 NumPy lines—no frameworks required. 𝕏
Manual backprop demystifies RL: it's linear algebra, not magic. 𝕏
Edge AI future favors lightweight scratch impls over bloated libs. 𝕏

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theAIcatchup

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#numpy rl #policy gradients #reinforce #reinforcement learning

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