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Introduction to Reinforcement Learning (2015)
- in3d 6y agoI watched this course and David Silver is a great lecturer, better than anybody else I’ve seen actually. I hope he does more publicly viewable courses in the future.
- foobaw 6y agoDavid Silver is awesome! Loved him in the Alpha Go documentary as well
- ddon 6y agoJust posting a link to a documentary: https://www.youtube.com/watch?v=WXuK6gekU1Y https://www.youtube.com/watch?v=WXuK6gekU1Y
- spicyramen 6y agoJust watched his interview with Lex, inspiring engineer
- jsemrau 6y agoThat's one of these moments in life where you see tech and you know it will change the world, but don't see the problem yet.
- vojta_letal 6y agoDoes world really work like that?
- jsemrau 6y agoWhen the first PC with Basic launched in the 80s many people wanted to develop for it. When the iPhone Appstore launched, many people started to build apps in the ecosystem. While it might be it bit too early to compare RL to those advances in technology. I personally feel there is huge potential. I might be wrong though. And I am fine with that.
- bonoboTP 6y agoRL isn't new though, the foundational results are about 25 years old.
- WanderPanda 6y agoAnd it feels a bit like it is stalling (at least in continuous control)
- cbames89 6y agoIn my opinion there's a wide open array of approaches from control that can help with this. Learning for Control is a new conference that looks at this very topic.
- dmarchand90 6y agoThat's still very analogous to the first PCs. By that point there had been decades of foundational computer work
- stevofolife 6y agoNo one said "new". You can apply what you said to PC and iPhones. Mainframes and palms existed before them.
- bitL 6y agoRL needs a supercomputer and its code is usually too fragile - making a trivial mistake anywhere (missing a constant multiplication, swapping the order of two consecutive lines of code etc.) would likely lead to your model never converging even if you got everything else right.
- patrick_halina 6y agoRL is a good theoretical solution for personalization: given a user state, select an action that maximizes a long term reward (eg. revenue/engagement.) It’s tricky building the implementations because unlike Go/Chess/Atari it’s hard to simulate humans. So you have to train the agents with batches of data offline (ie. using historic data from the agent’s past actions.) This is challenging because you don’t get as many chances to try different hyper parameters. It’s starting to be used more in industry though.
- jmeister 6y agoI’ve not kept up with the recent developments in this field - is Vowpal Wabbit widely used now? Any competitors? Or do people build their own in-house systems? Thanks
- flooo 6y agoVowpal Wabbit is used but many build something in-house into existing rule-based or supervised ml systems In green fields deployments, Azure personalizer may be a nice place to start looking. On the academic side, this paper provides an overview https://content.iospress.com/articles/data-science/ds200028 https://content.iospress.com/articles/data-science/ds200028
- jmeister 6y agoThank you, the review looks helpful.
- visarga 6y agoWatched the course and it's great, probably the best intro to RL. Multiple watches needed as the subject is very deep.
- captn3m0 6y agoI did the course during my RC batch this year. Highly recommended if you're looking to learn RL.
- hfkldjsjfkdj 6y agoWhen it comes to education online I prefer these, where they publish actual university course lectures and assignments than what you can find on Coursera and alike. There is still a big gap in the quality and depth.
- ilaksh 6y agoI used to be a bit more excited about RL. I mean, it's still definitely something I have to learn, but one aspect of it _seems_ lacking to me and is messing with my motivation to learn it. I'm sure someone will happily explain all the ways I am ignorant. It seems like there is a lot of emphasis on "direct RL" or whatever where they don't even really think about the model much, but it's I guess often inside of the policy or something? But it seems to me as someone who has just started learning about robotics, that I absolutely need to first verify that I have an accurate model of the environment which I can inspect. It seems like a lot of RL approaches might not even be able to supply that. I mean what I am stuck on as far as creating a robot (or virtual robot) is having a vision system that does all of the hard things I want. I feel like if I can detect edges and surfaces and shapes in 3D, parts of objects and objects, with orientation etc., and in a way I can display and manipulate it, that level of understanding will give me a firm base to build the rest of learning and planning on. I know all of that is very hard. It seems like they must have tried that for awhile and then kind of gave up to head down the current direction of RL? Or just decided it wasn't important. I still think it's important.
- bearzoo 6y agoI don't think people have given up on model based RL, it is just that describing a proper model is (like you are saying) very difficult. in the case you haven't seen or read the following: https://bair.berkeley.edu/blog/2019/12/12/mbpo/ https://bair.berkeley.edu/blog/2019/12/12/mbpo/
- howlin 6y agoOne thing to keep in mind about direct (learn the policy/behavior) versus indirect (learn the model and then simulate behaviors on the model to choose the best) is that sometimes it's much easier to find a good enough policy than it is to learn an accurate enough model for simulation. Driving is a good example of this. Most of the time all you need to do is stay in your lane and obey the rules for intersections. A simulation of a driving environment, on the other hand, is quite difficult.
- zekrioca 6y agoYou do not necessarily need to fully know the environment you are in, but you need to be able to evaluate how good the actions that you can take are in terms of an utility function. That’s how a RL algorithm can learn that going through a wall is a bad decision (reward(“ahead”) <= “$0“), and then decides for something else such as turning right or left (reward(“Left” || “right”) > “$0”). I think the main problem with RL is deciding if an utility function — as precise as it may be — can fully capture/estimate all nuances of an environment. Another problem is at adapting to the environment by having new actions added dynamically into your model and having it to converge as quickly as possible.
- luplex 6y agoI'm taking an adaptation of this class. My professor is simply reusing Silver's slides, so I'm watching the original lecture instead. Highly recommend!
- dgb23 6y agoAsking from a layman's perspective: I've read a bit about genetic algorithms or evolutionary computation at some point. Apparently it achieves good results as it can find discrete solutions for complex, well defined problems. Reinforcement learning is something I know even less about. But from what I gathered it is also most successful in well defined problems and systems (such as games). So my question is: How do they relate? Is there overlap and what are the most significant conceptual differences?
- computerphage 6y agoHmm... One way that I look at it is evolutionary computation is an optimization strategy. It's characterized by tracking a population of candidates, discarding the lowest scoring, mutating the survivors, and cross-combining elements from multiple candidates. RL is an optimization domain. It's the name of the problem, not the solution. You can straightforwardly use evolutionary algorithms on RL problems. However, a lot of the recent success in RL has come from using deep learning to try to solve various RL problems, not from trying evolutionary computation.
- dgb23 6y agoThat makes sense thank you! It's easy to conflate the concept/problem with the tool as an outsider.
- computerphage 6y agoAnd it doesn't help that popular sources are perfectly happy to just call things AI or RL and leave it at that. The popular discourse around AI sometimes sounds like "How do computers work? Programming. How does google work? Programming. How do games work? Programming." Like, they're not wrong, just too vague to be useful.
- suref 6y agoWhile RL is about the problem it's also about the solution. Problems/Environments are formulated in a way where methods can be applied easily (i.e. Markov decision process) and thus the solutions are directly connected to the way the problem is formulated. Deep learning is used for function approximation and is not in contrast with evolutionary computation. You can train a neutral network policy (mapping states to actions) with an evolutionary algorithm, but most of the success has come from methods that utilize the internal structure of the problem as mentioned earlier and evolutionary algorithms do not, which is what makes these optimization strategies both weak and powerful.
- hideo7746 6y agoOn one hand you're right, methods like Q-learning are model-free and do not necessarily encode much about state dynamics. The Q-function is a feature (function) of state and while ita may not say much about the model, it does encode the most important aspect of the model in terms of solving the task. Namely, it predicts the accumulated reward conditional on next actions actions. That makes it a somewhat narrow representation of state on its own. But, if you consider an environment that has many reward signals, and you learn Q functions for each, this ensemble of Q functions can consitute a rich representation of state. Depending on what the reward functions are, the associated Q functions may be sufficient to construct a full model. so I guess my point is that the learned quantities in RL encode key aspects of state, and when you expand beyond the single task/single reward RL setting the lines between value and model can become blurred.
- platz 6y agoHrm so are markov decision processes RL?
- computerphage 6y agoThat's one of RL's traditional formulations, yes. Bandits problems are another one. They've been generalized together into POMDPs partially observable Markov decision processes.
- jointpdf 6y agoThis is (roughly) the same course but the lectures are from 2018. The audio/video quality is quite a a bit better: https://youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb https://youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ...