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 primary purpose of passing a device to the agent class?

• To define the exploration strategy

• To set the initial step value

• To configure the code to run on CPU or GPU

• To determine the number of actions

What is the initial value of 'step' in the agent class?

• 0

• 10

• 1

• 5

How does the agent decide between exploration and exploitation?

• By comparing the exploration rate with a random number

• By using a fixed threshold

• By checking the number of actions

• By evaluating the device type

What is the role of 'argmax' in the action selection process?

• To compute the gradients

• To configure the device

• To initialize the step value

• To return the maximum Q value

Why is the action returned as a tensor?

• To avoid using random functions

• To increase the number of actions

• To simplify the code

• To ensure compatibility with TensorFlow

What happens if the exploration rate is less than the random number?

• The agent switches to CPU

• The agent increases the step value

• The agent returns the policy network's action

• The agent explores a random action

What is the purpose of sending the tensor to the device?

• To ensure it works on GPU if available

• To reduce the number of actions

• To initialize the strategy

• To compute the exploration rate