# AI Session

The objective is to give a very brief introduction to _DQN_ training using games (just to talk about theory), present _OpenSource Web_ interfaces and create _LLM Embeddings_ to ask questions about given documents.

Things to cover:
- Simple theory of Q-learning, with a practical example using the a Taxi game.
- Simple theory of Proximal Policy Optimization, with a practical example using a Pong game.
- _OpenSource Web_ interfaces available for Stable Diffusion, Inference and Embeddings.
- A practical example on how to create and use _LLMs_ and _Embeddings_ using a _Python_ script.

In no way should any of this be considered as a complete solution, the goal is just to provide enough elements for a start so all participants can grow from there.
