⇒ Reinforcement learning is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward.
⇒ In machine learning, reinforcement learning is one of those ideas that while not very widely applied in commercial applications yet today, is one of the pillars of machine learning.
State (s) → we call the position and orientation and speed and so on of the helicopter the state s.
Action (a) → the task is to find a function that maps from the state of the helicopter to an action a, meaning how far to push the two control sticks in order to keep the helicopter balanced in the air and flying and without crashing.
Reward → a key input to a reinforcement learning is something called the reward or the reward function which tells the helicopter when it's doing well and when it's doing poorly.it's the reinforcement learning algorithm's job to figure out how to get more of the good helicopter and fewer of the bad helicopter outcomes.
Why we can’t use supervised learning ?
<aside> 💡 so reinforcement learning has been used to get helicopters to fly a wide range of stunts or we call them aerobatic maneuvers.
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<aside> 💡 One way to think of why reinforcement learning is so powerful is you have to tell it what to do rather than how to do it. And specifying the reward function rather than the optimal action gives you a lot more flexibility in how you design the system.
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