Markov Decision Processes - Computerphile
163,019
2022-10-25に共有
Nick used an example from Mickael Randour: bit.ly/C_MickaelRandour
This video was previously called "Robot Decision Making"
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This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at www.bradyharan.com/
コメント (21)
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This guy was my lecturer about 10 years ago. He was very down to earth and explained the concepts in a really friendly way. Glad to see he's still doing it.
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This channel makes me appreciate the human brain more. We do all that automatically with barely a moment's thought.
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OMG as a Robotics student, I'm amazed how well explained that is. Love it <3
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Just took a RL course. Bellman equation and Markovian assumptions are so familiar. Btw, for those who are interested, the algorithm to solve discrete MDP (or model based RL problems in general) are Value Iterations and Policy Iterations, which are all based on Bellman equation.
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Where the formal definitions for concepts like MDP can get overwhelming , it really helps to have these easy to understand explanations
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I made these decisions for my real commute. The train was fastest, but occasionally much longer. The car was fast, but the cost of parking equalled 2 hours of work, so was effectively slowest. The latest I could leave and be sure of being on time was walking.
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Nice one, I met Professor Nick at Pembroke College Oxford. It was an honour.
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I heared a lot about MDP and policy functions in the context of reinforcement learning. But this is the best explanation I ever heared.
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MDP is the topic of my bachelorthesis and the example really helped understanding everything a lot better and I think I'll be using it throughout the thesis to understand the theory I have to write about. It's a lot easier to understand than some state a,b and c and action 1,2,3 :D
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This was a fantastic simple explanation, very enlightening.
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I rarely put a like on a video, but this one deserves it. I definitely want to hear more about the algorithms to solve MDP problems.
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Please, bring more from this guy
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There is a 3% chance that, somewhere along the route, there's a half-duplex roadblock because they're fixing the overhead wires or something. There's a 0.1% chance that a power line or tree fell across the road, forcing you to take an extremely long detour, but half of the time this happens, you could get past it on a bike.
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This is such a fascinating breakdown of Markov decision making. I love the mathematics that underpins Markov, but the creativity and imagination applied to the example and its host of solutions are delicious brain food.
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I'd like an autonomous taxi system that would decide it's all too hard to take me to the office, and would just take me back home, or, indeed, just refuse to take me to the office. "Sorry, I"m working from home today because the car refused to drive itself."
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You can read passion in every word he is pronouncing. Very good explanation.
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I literally had my final year project use a kalman filter to solve this problem. That's awesome! Edit: spelling
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the best explanation of this I've ever heard. many thanks.
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great video! Really well explained and interesting
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Fascination look into decision-making.
