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Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar, reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning. At OpenAI, we believe that deep learning generally, and deep reinforcement learning specifically, will play central roles in the development of powerful AI technology. To ensure that AI is safe, we have to come up with safety strategies and algorithms that are compatible with this paradigm. As a result, we encourage everyone who asks this question to study these fields. However, while there are many resources to help people quickly ramp up on deep learning, deep reinforcement learning is more challenging to break into.
Features
- An educational resource produced by OpenAI
- A short introduction to RL terminology, kinds of algorithms, and basic theory
- An essay about how to grow into an RL research role
- A curated list of important papers organized by topic
- A well-documented code repo of short, standalone implementations of key algorithms
- A few exercises to serve as warm-ups
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