Reinforcement Learning and Deep RL Python Theory and Projects - Agent Class Implemented

The video tutorial explains the implementation of an agent class in a reinforcement learning context. It covers defining strategies and actions, and how to select actions using exploration and exploitation strategies. The tutorial also discusses the use of CPU and GPU for running the code, and how to handle tensors in TensorFlow. The video concludes with a brief overview of the implemented agent class and hints at future lessons on creating a cart-pole environment manager.

What is the significance of returning actions in the form of tensors?

Describe the process of exploiting the policy network in the agent class.

What challenges are mentioned regarding the complexity of the agent class implementation?