What are the differences between RL and supervised learning?

Can you explain the main differences between reinforcement learning and supervised learning?

Sure! Reinforcement learning (RL) focuses on learning through interactions with an environment, where an agent takes actions to maximize cumulative rewards. In contrast, supervised learning involves learning from labeled data, where the model is trained to predict outcomes based on input-output pairs.

What are some examples of tasks suited for each type of learning?

For supervised learning, tasks like image classification or spam detection are common. In reinforcement learning, tasks like game playing or robotic control are typical examples.

How does the training process differ between the two?

In supervised learning, the model is trained on a fixed dataset with known labels, adjusting its parameters to minimize prediction errors. In RL, the agent learns by exploring the environment, receiving feedback in the form of rewards or penalties, and adjusting its strategy based on that feedback.

Can you give me a real-world application of reinforcement learning?

Absolutely! One notable application of reinforcement learning is in autonomous driving, where vehicles learn to navigate and make decisions based on real-time feedback from their environment.