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 new insight or understanding did you gain from this video?

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