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?

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

What criteria is mentioned for early stoppage in the algorithm?