Equivalence Between Policy Gradients and Soft Q-Learning

Soft Q-Learning and Policy Gradients — Turns Out They're One

There are two popular ways to teach a computer to learn from trial and error, called Q-learning and policy gradients.
For a long time they looked different, but new work shows that when you add a bit of randomness, the two methods actually become the soft (entropy-regularized) version of the same idea.
This is surprising because Q-values the computer learns often look wrong, yet the system still learns good behavior.
The trick is that both methods end up following the same learning rules, just written differently, so they guide learning in similar ways.

Researchers tested this on simple game tests and even on big arcade games, the results match or beat the usual tricks.
They even built a Q-learning system that learns like a popular policy method, without extra tricks such as target networks or rigid exploration schedules.
It means different recipes can give you the same cake, and that helps make smarter, simpler agents for learning tasks — like playing old school Atari games better, faster.

Read article comprehensive review in Paperium.net:
Equivalence Between Policy Gradients and Soft Q-Learning

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