Reinforcement Learning and Deep RL Python Theory and Projects - Evaluation and Testing

The video tutorial covers the evaluation and testing of a model using the evaluate policy method from a stable baseline evaluation class. It demonstrates how to pass a model and environment to the method, set the number of episodes, and render settings for Google Colab. The tutorial shows the model's performance improvement after training, with a significant increase in rewards and stability. It also explains how to test the model by predicting actions and updating states, achieving high accuracy. The video concludes with a brief mention of future steps, including adding callbacks and criteria for early stoppage.

How does the average reward change after training compared to the initial rewards?

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What was the score achieved after running the episodes in the testing phase?

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What criteria is mentioned for early stoppage in the algorithm?

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