Reinforcement Learning and Deep RL Python Theory and Projects - Train RL Model

The video tutorial covers the process of training a model using a dummy vectorized environment as a wrapper. It explains the setup of the model with PPO from the stable baseline, including defining the policy and setting hyperparameters. The training process is executed, and various metrics such as entropy loss, learning rate, and policy gradient loss are evaluated. The tutorial emphasizes the benefits of using built-in models for efficiency but also highlights the importance of understanding model implementation from scratch.

Describe the role of the environment in the training of the model.

What does the term 'verbose' refer to in the context of the training process?

What are some of the evaluation metrics mentioned that are provided during the training?