Reinforcement Learning and Deep RL Python Theory and Projects - Action

The video tutorial explores the concept of multiple agents in an environment, both in real-world scenarios and in reinforcement learning. It explains how agents interact with each other and the environment, using examples like sitting with a friend or racing cars. The tutorial delves into the actions available to agents, emphasizing the rules that govern these actions. It concludes with a task for students to analyze the environment, agents, and actions in the game Super Mario.

What is meant by having two agents in a reinforcement learning environment?

Can you provide a real-world example of two agents interacting?

What are the possible actions that an agent can take in a defined environment?

What is the significance of encoding actions with numeric values in reinforcement learning?

In the context of the blue dot agent, what limitations does it have regarding movement?

How does the set of rules for an agent's actions change with the environment?

Imagine a game like Super Mario. What are the environment, agent, and actions available in that game?