Key Papers in Deep Reinforcement Learning
The spinning up guide is neat, but it seems to assume access to fairly expensive GPU resources for running models.<p><a href="https://cloud.google.com/blog/products/gcp/new-lower-prices-for-gpus-and-preemptible-local-ssds" rel="nofollow">https://cloud.google.com/blog/products/gcp/new-lower-prices-...</a><p>> In US regions, each K80 GPU attached to a VM is priced at $0.45 per hour while each P100 costs $1.46 per hour.<p>The $300 free tier gets you ~600 hours of K80.  The spinning up guide suggests iterating models in <5 min, so that's 7200 iterations.<p>> start with vanilla policy gradient (also called REINFORCE), DQN, A2C (the synchronous version of A3C), PPO (the variant with the clipped objective), and DDPG, ... VPG...<p>that's 6 algorithms, combined with a half a dozen tasks to try, whittles it down to a few hundred iterations per task/algo combo.<p>that, combined with a lot of paper-reading, and perhaps clever blogging is probably enough to get started.<p>Still, it seems beneficial to democratize DL by making these 5 minute iterations free, doesn't it?