Backpropagation is a method for training neural networks, whereas reinforcement learning is a framework for learning from interaction with an environment.

In backpropagation, a neural network is trained to make predictions by adjusting the weights of its connections based on the error between its predictions and the true values. This is done through an iterative process called gradient descent, in which the weights are updated in a way that reduces the error. Backpropagation is typically used for supervised learning, where the network is given a labeled dataset and learns to make predictions based on this data.

Reinforcement learning, on the other hand, is used to learn from an environment through trial and error. An agent in a reinforcement learning system receives rewards or penalties for its actions, and it adjusts its behavior to maximize the reward. The agent does not have access to a labeled dataset or a teacher to correct its mistakes; it must learn through its own experience.