Reinforcement Learning and Deep RL Python Theory and Projects - Why RL

The video discusses the necessity of reinforcement learning despite the existence of supervised and unsupervised learning. It highlights two main advantages: learning from experience without supervision and adapting to uncertain environments. Reinforcement learning allows models to learn through trial and error, making it suitable for real-life applications where environments change unpredictably. The video concludes by promising examples of reinforcement learning in the next session.

What challenges do learners face when the environment changes over time?

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Discuss the two strong reasons for using reinforcement learning as mentioned in the text.

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