Reinforcement Learning and Deep RL Python Theory and Projects - Agent

The video tutorial introduces the concept of an agent in reinforcement learning, explaining its role in interacting with the environment. It discusses how agents can be represented in various contexts, such as self-driving cars and robotics, and the challenges they face, like navigating no-go areas. The tutorial also covers the concepts of rewards and punishments based on the agent's actions and concludes with a discussion on the possibility of multiple agents in one environment, setting the stage for future learning modules.

What does it mean to give credit to an agent in reinforcement learning?

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How does the agent's interaction with the environment define its role?

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Can there be multiple agents in one environment? Discuss from both real-world and reinforcement learning perspectives.

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