How is reinforcement learning different from supervised learning?

Can you explain how reinforcement learning differs from supervised learning?

Sure! Reinforcement learning focuses on learning through trial and error, receiving rewards or penalties, while supervised learning uses labeled data to train models.

What are some real-world applications of reinforcement learning?

Reinforcement learning is used in robotics, game playing, and autonomous vehicles, where agents learn to make decisions based on their environment.

Are there any specific algorithms used in reinforcement learning?

Yes, common algorithms include Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO). Each has its own strengths.

Can you give an example of supervised learning?

Certainly! A common example of supervised learning is image classification, where a model is trained on labeled images to recognize objects.