You can attach a GPU for free, and, if I recall, even a TPU. See https://colab.research.google.com/notebooks/gpu.ipynb
Quote:
"Iterate fast in simple environments. To debug your implementations, try them with simple environments where learning should happen quickly, like CartPole-v0, InvertedPendulum-v0, FrozenLake-v0, and HalfCheetah-v2 (with a short time horizon—only 100 or 250 steps instead of the full 1000) from the OpenAI Gym. Don’t try to run an algorithm in Atari or a complex Humanoid environment if you haven’t first verified that it works on the simplest possible toy task. Your ideal experiment turnaround-time at the debug stage is <5 minutes (on your local machine) or slightly longer but not much. These small-scale experiments don’t require any special hardware, and can be run without too much trouble on CPUs."
https://cloud.google.com/blog/products/gcp/new-lower-prices-...
> 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.
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.
> 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...
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.
that, combined with a lot of paper-reading, and perhaps clever blogging is probably enough to get started.
Still, it seems beneficial to democratize DL by making these 5 minute iterations free, doesn't it?