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Module 21: ES for RL & Policy Optimization - The OpenAI Bombshell

In 2017, OpenAI showed that a simple ES could match deep RL on MuJoCo tasks - with linear scaling across CPUs. No value functions, no replay buffers, no backpropagation through time. Just evolution. This module shows how and why.

Learning Objectives​

  1. Understand policy optimization as black-box optimization
  2. Implement OpenAI ES with shared noise tables
  3. Code fitness shaping for variance reduction
  4. Compare ES vs PPO on simple control tasks
  5. Understand when ES beats gradient-based RL (and when it doesn't)

Concept Explanation​

Coming soon.

Code Examples​

Coming soon.

Exercises​

Coming soon.

Milestone Checklist​

  • Implemented OpenAI ES for policy optimization
  • Understand shared noise tables and communication efficiency
  • Compared ES vs PPO
  • Can explain the parallelization advantage of ES for RL

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