What is reinforcement learning in robotics?

What is reinforcement learning in robotics?

Reinforcement learning in robotics is a type of machine learning where robots learn to make decisions by receiving rewards or penalties based on their actions.

How does it differ from supervised learning?

Unlike supervised learning, where the model learns from labeled data, reinforcement learning focuses on learning from the consequences of actions in an environment.

Can you give an example of reinforcement learning in action?

Sure! A common example is training a robot to navigate a maze. It receives positive feedback for reaching the goal and negative feedback for hitting walls.

What are some challenges in implementing reinforcement learning in robotics?

Challenges include the need for a large amount of training data, the complexity of environments, and ensuring safety during learning.