Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Evolution Strategies: A Fast, Scalable Alternative to Reinforcement Learning

Imagine teaching a robot or a game player by letting many trial runs happen at once.
That is the idea behind Evolution Strategies, a simple way to find good behaviors by trying lots of variations.
Because each worker only shares tiny numbers back, the method can scale to hundreds or thousands of computers without big slowdowns.
In practice this means some tasks that took days before can train much quicker — researchers solved a 3D walking task in about 10 minutes, and reached strong results on classic video games like Atari games after an hour.
The approach also shrug off delayed rewards and long tasks, so it works when feedback comes late or when episodes are very long.
It does not need complicated value estimates or fast action timing, which makes it easier to run and tune.
This won't replace every method, but for many problems it offers a practical, easy to scale path to get smart behaviors fast, especially when you got lots of machines to spare.

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Evolution Strategies as a Scalable Alternative to Reinforcement Learning

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