RL is also extremely useful in scenarios where rules cannot easily be made explicit. Think of riding a bike or flying a helicopter or gripping objects with a robot arm. Here we can more easily define the reward function - but the agent has to figure out how to do things (to maximise expected reward).
RL is also extremely useful in scenarios where rules cannot easily be made explicit. Think of riding a bike or flying a helicopter or gripping objects with a robot arm. Here we can more easily define the reward function - but the agent has to figure out how to do things (to maximise expected reward).