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.

Why is reinforcement learning considered a fundamental form of learning?

• It requires a large amount of labeled data.

• It learns from experience without supervision.

• It relies on historical data for training.

• It uses a fixed set of rules to learn.

What is a key limitation of supervised and unsupervised learning compared to reinforcement learning?

• They require more computational power.

• They are more prone to overfitting.

• They need a dynamic environment to function.

• They cannot learn from experience directly.

How does reinforcement learning handle changes in the environment?

• By using a static set of rules to adjust.

• By relying on pre-collected data to make predictions.

• By adapting and learning from the new environment.

• By ignoring the changes and sticking to the initial model.

In what way is reinforcement learning different from supervised learning regarding data requirements?

• Reinforcement learning requires continuous data input.

• Reinforcement learning requires labeled data.

• Reinforcement learning does not require any data.

• Reinforcement learning requires a fixed dataset.

What makes reinforcement learning suitable for uncertain environments?

• Its dependence on historical data for decision making.

• Its ability to predict future changes accurately.

• Its reliance on a stable and unchanging environment.

• Its capability to adapt and learn from environmental changes.