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Show HN: Multi-GPU Reinforcement Learning in Tensorflow for OpenAI Gym

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Re: Show HN: Multi-GPU Reinforcement Learning in Tensorflow for OpenAI Gym

#6
post #2

A bit off topic, but how many people here use rl in their day job, and, if you use it, what do you use it for?

I work on a few algorithms that could be classified as RL given an open mind. Most of them learn distributions from streaming data via some kind of online EM. I know that people in the ad-serving, porn-serving, and website optimization (A/B stuff) sectors use RL pretty extensively as well, but I'm not one of them at the moment.

Re: Show HN: Multi-GPU Reinforcement Learning in Tensorflow for OpenAI Gym

#7
post #2

A bit off topic, but how many people here use rl in their day job, and, if you use it, what do you use it for?

Used simple RL (finite states, no neural nets) for gamedev - finding optimal policies for player behavior helps in fixing balance issues.

Re: Show HN: Multi-GPU Reinforcement Learning in Tensorflow for OpenAI Gym

#8
post #2

A bit off topic, but how many people here use rl in their day job, and, if you use it, what do you use it for?

I work on a few algorithms that could be classified as RL given an open mind. Most of them learn distributions from streaming data via some kind of online EM. I know that people in the ad-serving, porn-serving, and website optimization (A/B stuff) sectors use RL pretty extensively as well, but I'm not one of them at the moment.

> learn distributions from streaming data

That's unsupervised learning afaik - clustering, manifolds etc. Where is "reinforcement" part there (agent, environment, reward)?

Re: Show HN: Multi-GPU Reinforcement Learning in Tensorflow for OpenAI Gym

#10
post #2

A bit off topic, but how many people here use rl in their day job, and, if you use it, what do you use it for?

We're using it for web crawling: define what to look for (a reward function), and crawler can learn how to get these pages from the web without wasting too much HTTP requests for irrelevant content. No neural nets, just Q-Learning with linear function approximation, with some common tricks like double learning and experience replay.