Policy Gradient Made Easy: From Bikes to Language Models

A friendly, intuition‑first tour of the policy gradient theorem in reinforcement learning. We use bike‑riding analogies, simple explanations, and practical Python code to show how log-probabilities, Monte Carlo sampling, and reward signals guide learning—even when the “good” score is fuzzy. We’ll walk through how human feedback can train language models, and discuss how this framework might apply to personal goals as a broader way to turn intuition into concrete updates.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

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