What is the REINFORCE algorithm?

REINFORCE is a policy gradient method that directly optimizes action probabilities using Monte Carlo reward estimates—no value functions needed.

How to implement policy gradients in NumPy?

Use sigmoid for probs, sample actions, compute dlogp = action - prob, discount/standardize rewards, backprop weighted grads with RMSProp.

Does REINFORCE work on CartPole?

Yes—trains to 500-step max in ~3000 episodes with tuned hyperparams like gamma=0.99 and batch_size=5.

🤖 AI & Machine Learning

REINFORCE from Scratch: Mastering Policy Gradients in Raw NumPy

Imagine balancing a wobbly pole on a speeding cart, all with code you wrote by hand in NumPy. Policy gradients make it happen, flipping RL on its head without Q-values or fancy libraries.

theAIcatchup Apr 08, 2026 4 min read

CartPole agent balancing pole perfectly after REINFORCE training in NumPy

⚡ Key Takeaways

Implement full REINFORCE in 100 lines of NumPy: forward, backprop, RMSProp—no frameworks. 𝕏
Policy gradients excel for continuous actions where Q-learning's argmax fails. 𝕏
From-scratch coding builds RL intuition, predicting innovations beyond black-box tools. 𝕏

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theAIcatchup

Community-driven. Code-first.

#cartpole #numpy rl #policy gradients #reinforce #reinforcement learning

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Originally reported by Dev.to

⚡ Key Takeaways

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