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 new insight or understanding did you gain from this video?