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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Rishu Shrivastav
5
explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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Vrushali Kandesar
5
Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
5
Amazing Teaching
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Juboraj Juboraj
5
Easy to understand & explain details.
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Joydeb
5
Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
5
Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
5
Kushal is very good explainer he is covering all topics nicely 👍
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