Reinforcement Learning and Deep RL Python Theory and Projects - Setting Up Environment

The video tutorial covers setting up an environment for a stock-related project, focusing on defining frame bounds and window sizes. It explains the concept of window size, its role in data prediction, and the importance of starting frame bounds from the window size. The tutorial addresses error handling, identifies signals and features for the model, and explores the action space, which includes discrete actions like buying and selling. The video concludes with a brief mention of setting up a random environment in future videos.

What are the features that the model will work with in the environment?

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How does the action space relate to the decisions made in the model?

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