reinforcement learning - RL
What is reinforcement learning? Is it different from using gradient dissent to optimize parameter values?
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions based on its past experiences (exploration) and new choices (exploitation).
On the other hand, gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.
So, reinforcement learning is a paradigm of learning while gradient descent is an optimization strategy. They are not mutually exclusive and can be used together. For example, in reinforcement learning, we often need to optimize a reward function, and this optimization could be done using gradient descent.