Reinforcement Learning and Deep RL Python Theory and Projects - Environment

The video tutorial explores the concept of 'environment' by defining it as the surroundings or conditions in which agents (people, animals, or plants) operate. It uses a game board analogy to explain how environments function in reinforcement learning, emphasizing the importance of rules and constraints. The tutorial discusses how agents interact within these environments, highlighting movement restrictions and the necessity of rules for interaction. The video concludes by reiterating that environments consist of surroundings and a set of rules, both in real life and in created scenarios.

What new insight or understanding did you gain from this video?

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