Q learning is a value-based way of delivering information to help an agent decide which action to take. Let's look at an example to better understand this method: In a building, there are five rooms that are connected by doors.
Taking opposite actions suggests updating two Q-values at the same time. The agent will update the Q-value for each action and its inverse action, speeding up the learning process. The renowned test-bed grid world problem is reproduced using a revolutionary Q-learning method based on the concept of opposite action.
One of Q-advantages Learning's is that it can compare the expected utility of various actions without the need for a model of the environment. Reinforcement Learning is a method of problem solving in which the agent learns without the assistance of a tutor.
When given a state x, you learn the projected cost via value iteration. When you use q-learning and take action a while in state x, you get the promised discounted cost.
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I want a clearity up on these
1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.
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how can i download the finaldata.csv?
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