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Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | And when I model some aspect of it in a certain way, that gives me some set of algorithms I can | https://karpathy.ai/lexicap/0015-large.html#00:19:12.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | use. You can model the world in all kinds of ways. Some have, some are, some are, some are | https://karpathy.ai/lexicap/0015-large.html#00:19:17.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | more accepting of uncertainty, more easily modeling uncertainty of the world. Some really force the | https://karpathy.ai/lexicap/0015-large.html#00:19:26.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | world to be deterministic. And so certainly MDPs model the uncertainty of the world. Yes. Model | https://karpathy.ai/lexicap/0015-large.html#00:19:33.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | some uncertainty. They model not present state uncertainty, but they model uncertainty in the | https://karpathy.ai/lexicap/0015-large.html#00:19:42.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | way the future will unfold. Right. So what are Markov decision processes? So Markov decision | https://karpathy.ai/lexicap/0015-large.html#00:19:47.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | process is a model. It's a kind of a model that you could make that says, I know completely the | https://karpathy.ai/lexicap/0015-large.html#00:19:54.560 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | current state of my system. And what it means to be a state is that I, that all the, I have all | https://karpathy.ai/lexicap/0015-large.html#00:19:59.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the information right now that will let me make predictions about the future as well as I can. | https://karpathy.ai/lexicap/0015-large.html#00:20:05.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | So that remembering anything about my history wouldn't make my predictions any better. | https://karpathy.ai/lexicap/0015-large.html#00:20:11.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | And, but then it also says that then I can take some actions that might change the state of the | https://karpathy.ai/lexicap/0015-large.html#00:20:17.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | world. And that I don't have a deterministic model of those changes. I have a probabilistic | https://karpathy.ai/lexicap/0015-large.html#00:20:23.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | model of how the world might change. It's a, it's a useful model for some kinds of systems. | https://karpathy.ai/lexicap/0015-large.html#00:20:28.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | I think it's a, I mean, it's certainly not a good model for most problems, I think, because for most | https://karpathy.ai/lexicap/0015-large.html#00:20:34.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | problems you don't actually know the state. For most problems you, it's partially observed. So | https://karpathy.ai/lexicap/0015-large.html#00:20:42.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that's now a different problem class. So, okay. That's where the POMDPs, the part that we observe | https://karpathy.ai/lexicap/0015-large.html#00:20:48.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | with the Markov decision processes step in. So how do they address the fact that you can't | https://karpathy.ai/lexicap/0015-large.html#00:20:55.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | observe most incomplete information about most of the world around you? Right. So now the idea is | https://karpathy.ai/lexicap/0015-large.html#00:21:01.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | we still kind of postulate that there exists a state. We think that there is some information | https://karpathy.ai/lexicap/0015-large.html#00:21:07.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | about the world out there such that if we knew that we could make good predictions, but we don't | https://karpathy.ai/lexicap/0015-large.html#00:21:12.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | know the state. And so then we have to think about how, but we do get observations. Maybe I get | https://karpathy.ai/lexicap/0015-large.html#00:21:18.000 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | images or I hear things or I feel things, and those might be local or noisy. And so therefore | https://karpathy.ai/lexicap/0015-large.html#00:21:23.680 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | they don't tell me everything about what's going on. And then I have to reason about given the | https://karpathy.ai/lexicap/0015-large.html#00:21:29.680 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | history of actions I've taken and observations I've gotten, what do I think is going on in the | https://karpathy.ai/lexicap/0015-large.html#00:21:34.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | world? And then given my own kind of uncertainty about what's going on in the world, I can decide | https://karpathy.ai/lexicap/0015-large.html#00:21:39.920 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | what actions to take. And so how difficult is this problem of planning under uncertainty in your | https://karpathy.ai/lexicap/0015-large.html#00:21:43.760 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | view and your long experience of modeling the world, trying to deal with this uncertainty in | https://karpathy.ai/lexicap/0015-large.html#00:21:50.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | especially in real world systems? Optimal planning for even discrete POMDPs can be undecidable | https://karpathy.ai/lexicap/0015-large.html#00:21:57.840 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | depending on how you set it up. And so lots of people say, I don't use POMDPs because they are | https://karpathy.ai/lexicap/0015-large.html#00:22:05.040 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | intractable. And I think that that's kind of a very funny thing to say because the problem you | https://karpathy.ai/lexicap/0015-large.html#00:22:12.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | have to solve is the problem you have to solve. So if the problem you have to solve is intractable, | https://karpathy.ai/lexicap/0015-large.html#00:22:19.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that's what makes us AI people, right? So we solve, we understand that the problem we're | https://karpathy.ai/lexicap/0015-large.html#00:22:24.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | solving is wildly intractable that we can't, we will never be able to solve it optimally, | https://karpathy.ai/lexicap/0015-large.html#00:22:28.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | at least I don't. Yeah, right. So later we can come back to an idea about bounded optimality | https://karpathy.ai/lexicap/0015-large.html#00:22:34.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | and something. But anyway, we can't come up with optimal solutions to these problems. | https://karpathy.ai/lexicap/0015-large.html#00:22:41.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | So we have to make approximations, approximations in modeling, approximations in the solution | https://karpathy.ai/lexicap/0015-large.html#00:22:45.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | algorithms and so on. And so I don't have a problem with saying, yeah, my problem actually, | https://karpathy.ai/lexicap/0015-large.html#00:22:50.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it is POMDP in continuous space with continuous observations. And it's so computationally complex, | https://karpathy.ai/lexicap/0015-large.html#00:22:56.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | I can't even think about it's, you know, big O whatever. But that doesn't prevent me from, | https://karpathy.ai/lexicap/0015-large.html#00:23:02.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it helps me, gives me some clarity to think about it that way and to then take steps to | https://karpathy.ai/lexicap/0015-large.html#00:23:09.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | make approximation after approximation to get down to something that's like computable | https://karpathy.ai/lexicap/0015-large.html#00:23:16.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | in some reasonable time. When you think about optimality, the community broadly has shifted on | https://karpathy.ai/lexicap/0015-large.html#00:23:20.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that, I think a little bit in how much they value the idea of optimality, of chasing an optimal | https://karpathy.ai/lexicap/0015-large.html#00:23:26.720 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | solution. How has your views of chasing an optimal solution changed over the years when | https://karpathy.ai/lexicap/0015-large.html#00:23:34.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | you work with robots? That's interesting. I think we have a little bit of a methodological crisis | https://karpathy.ai/lexicap/0015-large.html#00:23:40.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | actually from the theoretical side. I mean, I do think that theory is important and that right now | https://karpathy.ai/lexicap/0015-large.html#00:23:48.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | we're not doing much of it. So there's lots of empirical hacking around and training this and | https://karpathy.ai/lexicap/0015-large.html#00:23:53.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | doing that and reporting numbers, but is it good? Is it bad? We don't know. It's very hard to say | https://karpathy.ai/lexicap/0015-large.html#00:24:00.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | things. And if you look at like computer science theory, so people talked for a while, everyone was | https://karpathy.ai/lexicap/0015-large.html#00:24:05.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | about solving problems optimally or completely. And then there were interesting relaxations. So | https://karpathy.ai/lexicap/0015-large.html#00:24:16.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | people look at, oh, are there regret bounds or can I do some kind of approximation? Can I prove | https://karpathy.ai/lexicap/0015-large.html#00:24:22.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | something that I can approximately solve this problem or that I get closer to the solution as | https://karpathy.ai/lexicap/0015-large.html#00:24:30.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | I spend more time and so on? What's interesting I think is that we don't have good approximate | https://karpathy.ai/lexicap/0015-large.html#00:24:34.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | solution concepts for very difficult problems. I like to say that I'm interested in doing a very | https://karpathy.ai/lexicap/0015-large.html#00:24:42.560 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | bad job of very big problems. Right. So very bad job, very big problems. I like to do that, | https://karpathy.ai/lexicap/0015-large.html#00:24:51.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | but I wish I could say something. I wish I had a, I don't know, some kind of a formal solution | https://karpathy.ai/lexicap/0015-large.html#00:25:00.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | concept that I could use to say, oh, this algorithm actually, it gives me something. | https://karpathy.ai/lexicap/0015-large.html#00:25:09.120 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | Like I know what I'm going to get. I can do something other than just run it and get out. | https://karpathy.ai/lexicap/0015-large.html#00:25:16.240 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | So that, that notion is still somewhere deeply compelling to you. The notion that you can say, | https://karpathy.ai/lexicap/0015-large.html#00:25:19.840 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | you can drop thing on the table says this, you can expect this, this algorithm will | https://karpathy.ai/lexicap/0015-large.html#00:25:27.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | give me some good results. I hope there's, I hope science will, I mean, | https://karpathy.ai/lexicap/0015-large.html#00:25:32.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | there's engineering and there's science. I think that they're not exactly the same. | https://karpathy.ai/lexicap/0015-large.html#00:25:37.280 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | And I think right now we're making huge engineering, like leaps and bounds. So the | https://karpathy.ai/lexicap/0015-large.html#00:25:42.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | engineering is running away ahead of the science, which is cool. And often how it goes, right? So | https://karpathy.ai/lexicap/0015-large.html#00:25:47.040 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | we're making things and nobody knows how and why they work roughly, but we need to turn that into | https://karpathy.ai/lexicap/0015-large.html#00:25:52.240 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | science. There's some form. It's a, yeah, there's some room for formalizing. We need to know what | https://karpathy.ai/lexicap/0015-large.html#00:25:59.680 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the principles are. Why does this work? Why does that not work? I mean, for a while, people built | https://karpathy.ai/lexicap/0015-large.html#00:26:05.440 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | bridges by trying, but now we can often predict whether it's going to work or not without building | https://karpathy.ai/lexicap/0015-large.html#00:26:09.840 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | it. Can we do that for learning systems or for robots? So your hope is from a materialistic | https://karpathy.ai/lexicap/0015-large.html#00:26:14.480 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | perspective that intelligence, artificial intelligence systems, robots are just fancier | https://karpathy.ai/lexicap/0015-large.html#00:26:20.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | bridges. Belief space. What's the difference between belief space and state space? So you | https://karpathy.ai/lexicap/0015-large.html#00:26:27.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | mentioned MDPs, FOMDPs, reasoning about, you sense the world, there's a state. | https://karpathy.ai/lexicap/0015-large.html#00:26:33.040 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | Uh, what, what's this belief space idea? That sounds so good. | https://karpathy.ai/lexicap/0015-large.html#00:26:39.840 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | It sounds good. So belief space, that is instead of thinking about what's the state of the world | https://karpathy.ai/lexicap/0015-large.html#00:26:44.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | and trying to control that as a robot, I think about what is the space of beliefs that I could | https://karpathy.ai/lexicap/0015-large.html#00:26:51.600 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | have about the world. What's, if I think of a belief as a probability distribution of our ways | https://karpathy.ai/lexicap/0015-large.html#00:26:58.880 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the world could be, a belief state is a distribution. And then my control problem, if I'm reasoning | https://karpathy.ai/lexicap/0015-large.html#00:27:03.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | about how to move through a world I'm uncertain about, my control problem is actually the problem | https://karpathy.ai/lexicap/0015-large.html#00:27:10.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | of controlling my beliefs. So I think about taking actions, not just what effect they'll have on the | https://karpathy.ai/lexicap/0015-large.html#00:27:16.160 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | world outside, but what effect they'll have on my own understanding of the world outside. And so | https://karpathy.ai/lexicap/0015-large.html#00:27:21.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | that might compel me to ask a question or look somewhere to gather information, which may not | https://karpathy.ai/lexicap/0015-large.html#00:27:26.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | really change the world state, but it changes my own belief about the world. That's a powerful way | https://karpathy.ai/lexicap/0015-large.html#00:27:32.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | to, to empower the agent, to reason about the world, to explore the world. So what kind of | https://karpathy.ai/lexicap/0015-large.html#00:27:38.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | problems does it allow you to solve to, to consider belief space versus just state space? | https://karpathy.ai/lexicap/0015-large.html#00:27:46.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | Well, any problem that requires deliberate information gathering, right? So if in some | https://karpathy.ai/lexicap/0015-large.html#00:27:52.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | problems like chess, there's no uncertainty, or maybe there's uncertainty about the opponent, | https://karpathy.ai/lexicap/0015-large.html#00:27:58.400 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | there's no uncertainty about the state. And some problems, there's uncertainty, | https://karpathy.ai/lexicap/0015-large.html#00:28:05.040 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | but you gather information as you go, right? You might say, Oh, I'm driving my autonomous car down | https://karpathy.ai/lexicap/0015-large.html#00:28:10.320 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the road and it doesn't know perfectly where it is, but the light hours are all going all the time. | https://karpathy.ai/lexicap/0015-large.html#00:28:16.080 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | So I don't have to think about whether to gather information. But if you're a human driving down | https://karpathy.ai/lexicap/0015-large.html#00:28:20.560 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | the road, you sometimes look over your shoulder to see what's going on behind you in the lane. | https://karpathy.ai/lexicap/0015-large.html#00:28:25.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | And you have to decide whether you should do that now. And you have to trade off the fact that | https://karpathy.ai/lexicap/0015-large.html#00:28:31.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | you're not seeing in front of you and you're looking behind you and how valuable is that | https://karpathy.ai/lexicap/0015-large.html#00:28:37.840 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | information and so on. And so to make choices about information gathering, you have to reasonably | https://karpathy.ai/lexicap/0015-large.html#00:28:41.520 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | space. Also, I mean, also to just take into account your own uncertainty before trying to | https://karpathy.ai/lexicap/0015-large.html#00:28:47.200 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | do things. So you might say, if I understand where I'm standing relative to the door jam, | https://karpathy.ai/lexicap/0015-large.html#00:28:56.800 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | pretty accurately, then it's okay for me to go through the door. But if I'm really | https://karpathy.ai/lexicap/0015-large.html#00:29:05.360 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | not sure where the door is, then it might be better to not do that right now. | https://karpathy.ai/lexicap/0015-large.html#00:29:08.640 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | The degree of your uncertainty about the world is actually part of the thing you're trying to | https://karpathy.ai/lexicap/0015-large.html#00:29:12.960 |
Leslie Kaelbling: Reinforcement Learning, Planning, and Robotics | Lex Fridman Podcast #15 | optimize in forming the plan, right? So this idea of a long horizon of planning for a PhD or just | https://karpathy.ai/lexicap/0015-large.html#00:29:17.760 |
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