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?

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