Evolution Strategies
📰 Lilian Weng's Blog
Evolution Strategies (ES) can be used for optimizing model parameters when gradients are unknown or cannot be computed directly
Action Steps
- Identify problems where gradients are unknown or cannot be computed directly
- Explore classic ES methods such as gradient-free optimization
- Apply ES to deep reinforcement learning tasks
Who Needs to Know This
Machine learning engineers and researchers can benefit from ES, especially in deep reinforcement learning scenarios where traditional gradient descent methods may not be applicable
Key Insight
💡 ES can be used as an alternative to gradient descent in certain scenarios
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🤖 Evolution Strategies: optimize model params without gradients!
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