Hello Gimpei. Spice.ai currently supports two algorithms, Vanilla Policy Gradient and Deep Q-Learning. It provides an interface to plug in your own algorithms, though. We're looking to add more to it as we go along.

You can find more info at https://docs.spiceai.org/deep-learning-ai/

I think you are going to confuse people by framing this as "time series" and then focussing on reinforcement learning. A lot/most of your framing is around RL.

from a first glance and a read through your roadmap, this does not feel suitable for people who know what they are doing with RL. It also does not feel suitable for people who don't know what they are doing with RL.

How about for people who know what they are doing with time series?

reinforcement learning deals with sequential decision making to maximize future rewards, time series data is any data over time

you should be more clear in your title because the two domains are very different

Can you elaborate more on why you choose those? . More specifically: Did you benchmark them using known datasets? (Against other algorithms?)

Also, where do you store the training data?

We chose them because they were fairly straightforward to implement and different enough from one another that we could ensure our interface generalized well.

Re benchmarking - at this point we're looking to show directionality, not necessarily blinding speed. We intend to get the tooling feeling right, then work to optimize perf.

Right now, training data comes from the local disk, InfluxDB, or can be piped in from your application via our API. We're looking to build out a set of community-driven components for streaming and processing data. You can learn more about that here - https://github.com/spiceai/data-components-contrib

We'd love for you to contribute!

Here is that part of the repo:

https://github.com/spiceai/spiceai/blob/trunk/ai/src/algorit...