Original subtitles

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ہیلو مصنوعی ذہانت کے کورس میں دوبارہ خوش آمدید۔

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Today we're going to discuss the plan of attack for the section we're talking about kill learning.

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And we've got quite a few tutorials so I think it is a good idea for us to quickly go through them to

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understand what to expect in the upcoming videos.

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So here we go.

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All right.

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What we will learn in this section.

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First things first we will talk about what reinforcement learning actually is and what the philosophic

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behind reinforcement learning is and how reinforcement learning actually can be seen in real life and

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how it relates to things that we observe in real life are actually things that we do ourselves.

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They don't talk about the bellmen equation very fundamental concept underpinning everything or a lot

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of things that are happening and for reinforcement learning especially in the space of CULE learning

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and what we're going to be discussing in this section of the course and in the following sections.

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Then we'll talk about the plan and the plan that and raw iron artificial intelligence comes up with

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in order to navigate inside environments we'll see what that how that comes together very quick but

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quite interesting.

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There we'll talk about market of decision processes and your concept we're going to introduce a very

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new concept which will slowly even add a layer of sophistication to our Belman equation to our whole

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reinforcement learning to our CULE learning concepts and that's the way this section is structured that

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we introduce the Bollmann equation a very simplistic form and then slowly throughout the tutorials we

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adds layers of sophistication to it in order to get to the final version.

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That is our designated destination in terms of Hillary but we'll get there slowly.

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In order for us to have enough time to process all that information and let it settle in.

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And mark of dissident proses is an extra layer of sophistication on top of what we've discussed or what

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we will have or it discussed by then there will talk about policies versus plans.

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Another interesting Tauriel they're all interesting.

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Just another quick tutorial on how policy is different from plans and what the differences there are

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and these are terms that you will probably hear or read in the literature if you're going to be delving

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into it to get additional information on reinforcement learning.

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They're all talk about adding a living penalty to our environments.

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And that's that's kind of another way of adding complexity into the environments that our agents are

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going to be operating in.

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They're all talk about the intuition behind keep learning so up until that tutorial we're going to be

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talking values of states.

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And then finally we're going to switch to talking about values or actions or cube values and then we're

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going to introduce the temporal difference.

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This is a tutorial where everything that we've learned is going to come together to explain how exactly

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do agents or artificial does artificial intelligence learn how does it update its values through all

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the iterative process that is going through.

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And then finally we're going to look at a visible zation of learning so we're going to take everything

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we learn and we're going to look at it happen in front of our eyes and watch an artificial intelligence

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actually perform CULE learning and do all the things that we're going to discuss on an intuitive level

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is going to actually do in practice and that will help us even further grasp that knowledge that we're

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going to be coming off in the section.

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So hopefully you're very excited about these upcoming tutorials.

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I definitely am.

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And there some very interesting slides coming up and more important the concepts themselves are very

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very interesting and I'm sure you're going to enjoy them quite a lot.

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And I look forward to seeing you next time.

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Until then enjoy AI.