Reinforcement Learning and Deep RL Python Theory and Projects - Episode

The video tutorial discusses the concept of an episode in reinforcement learning, where an agent moves through various states until it reaches a 'done' state, either by achieving a goal or entering a dead state. The tutorial explains how episodes are structured, the conditions for their completion, and the learning process of the agent. It also covers potential infinite loops and step limits. The video concludes with a preview of the next module, which will involve implementing a game using these concepts.

What new insight or understanding did you gain from this video?