7 ms·
Agent57: Outperforming the human Atari benchmark
- unwoundmouse 7y agoWould it be fair to say this is a resnet moment for rl
- WnZ39p0Dgydaz1 7y agoThis seems more like overfitting a complex system to a specific problem set.
- KaoruAoiShiho 7y agoThis feels like the typical HN less storage than a Nomad comment. To me this system reads as very important and necessary for the development of AI in general.
- WnZ39p0Dgydaz1 7y agoMeh, I'll take the bait. There are no fundamental new ideas in this paper compared to the preceding papers. What they do is to tune hyperparameters (BPTT length, exploration/exploitation tradeoff, and policy parameterization) in a smart fashion as to fit the bottom 5% of Atari games. Obviously the parameters, or equivalently, architecture choices, are tuned to achieve exactly that - good performance at the bottom 5% of Atari games. None of these choices will generalize outside of this specific set of Atari games. The reasons we are doing badly at these games are well-understood. They typically require "world knowledge" (what is a key? what is a door?) and reasoning (I found a key, that can be used to open a door). That is, the visual representations need to encode such knowledge. Algorithms don't possess this world knowledge as they are not embodied in our world, so they need to learn it from scratch, i.e. brute-force it. That's exactly what this paper is doing - brute-forcing the solutions by finding just the right hyperparameters with millions of hours worth of compute. A good analogy is what would happen if you took the game but flipped the pixels in some deterministic way so that the screen would look like noise to a human. A key would no longer be a key, but the structure is still the same. If someone asked you to solve Montezuma's revenge with that representation, you would not be able to. Does that make you stupid or non-human? So, because these games require human world knowledge, solving them in the same way as simpler games is kind of besides the point.
- KaoruAoiShiho 7y agoThanks for explaining your take but this sounds very reductive. When it comes down to it every problem is solved by tuning for that problem specifically. While Never Give Up (NGU) is not fundamentally new, it is an important step in computers learning. You need to be able to generalize solutions to problems where you don't have contextual information. Imagine if you were a caveman and asked to operate an iPhone. You're not stupid if you don't know how, but if I tell you, "never give up", and put you in a room for 5 years I'd expect some results from a sentient being. This is an important process too. We would be much closer to good AI if it can figure things out by itself instead of being constantly fed "clean" data.
- WnZ39p0Dgydaz1 7y agoThere's a difference between research and engineering. Is this system impressive? Definitely! It's a complex engineering effort, and highly tuned to solve a specific problem - beat the Atari benchmark. Does it teach us anything fundamentally new? No, it still has horrible sample complexity and does not generalize to anything outside of Atari unless you completely re-tune it. And I don't mean re-train. I mean changing the architecture and assumptions. That's different from projects such as e.g. AlphaZero or MuZero. IMO this would've been more appropriate to be published as an open-source system so that it can be applied and tuned to other problems as opposed to a research paper. As research, nobody outside of DeepMind can ever reproduce this. You are completely changing the topic with this: > While Never Give Up (NGU) is not fundamentally new, it is an important step in computers learning. You need to be able to generalize solutions to problems where you don't have contextual information We're not even talking about NGU, we're talking about the paper linked in this post. This specific paper proposes little new in that regard. It just engineers a system to do this specifically for Atari games by taking a previous paper and changing some parameters. Neat, but it's not some kind of breakthrough.
- visarga 7y ago> Does it teach us anything fundamentally new? No, it still has horrible sample complexity and does not generalize to anything outside of Atari unless you completely re tune it. I thought it was interesting that they let the agent learn the exploration/exploitation tradeoff, also combining memory and intrinsic motivation. Another contribution of this paper would be that they showed all these tricks can build on each other, thus, are complementary. Humans brains are also a bag of tricks fine-tuned to the goal and requirements of making more humans.
- MasterScrat 7y agoIt's not really... They make a very nice and clear summary of the current state of RL, then introduce an incremental improvement by combining existing approaches and throwing a lot of compute at the problem. Keep in mind DeepMind has a lot of money for PR - but nice prose and diagrams shouldn't affect your judgement of whether something is important or not!
- twic 7y agoDoes anyone still have an iPod?
- trevyn 7y agoOk, maybe it's time to slow down and consider if this is going in a good direction, before just building whatever it is that can be built?
- unwoundmouse 7y agoMoloch whose mind is pure machinery! Moloch whose blood is running money! Moloch whose fingers are ten armies! Moloch whose breast is a cannibal dynamo! Moloch whose ear is a smoking tomb!
- sytelus 7y agoThis whole evolution looks more and more like expert systems from 1980s where people kept adding more and more complexity to "solve" a specific problem. For RL, we started with simple DQN that was elegant but now the new algorithms looks like a massive hodge podge of band aids. NGU, as it is, extraordinarily complex and looks adhoc mix of various patches. Now on the top of NGU, we are also throwing in meta-controller and even bandits among other things to complete the proverbial kitchen sink. Sure, we get to call victory on Atari but this is far and away from elegant and beautiful. It would be surprising if this victory generalizes to other problems where folks have built different "expert systems" specific to those problems. So all this feels a lot like Watson winning jeopardy moment to me... PS: Kudos to DeepMind for pushing for median or even betten bottom percentile instead of simplistic average metric which also hides variance.
- chpatrick 7y agoDoesn't the human brain have millions of years of band aids?
- jacobush 7y agoYes and at this pace it may take thousands (or at least hundreds) of years to patch up our A.I. to general A.I.
- deleted 7y ago[deleted]
- eli_gottlieb 7y agoNo, most likely not. Our best theories of the brain are both computationally and evolutionarily parsimonious.
- gnramires 7y agoYou can make a simple theory of anything. An airplane as a spherical ball of varying friction with a steerable propulsion. Or a massive electro-mechanical system. There's a fidelity curve as you progress in complexity. Fidelity curve of a literal ball stabilizes quickly, while the fidelity curve of an airplane extends far. The brain is massively complex, we still don't understand the ways that it computes and learns in detail except generalities such as using electrical and chemical signals, neurotransmissors, and some knowledge of architecture and region differentiation. Those models all point to an extremely complex system with quite a number of different specialized parts, with neurons individually having quite amazing complexity (and so on for capsules, neuronal systems). I think it's unlikely a "simple" system (if you could consider a vanilla CNN simple, for example) will solve human-like AGI efficiently (either data-efficiently or computationally efficiently). There are too many requirements. Also, while simplicity is good... a researcher shouldn't be caught obsessing over it exclusively, as it is not the end goal, which personally would be the advancement of understanding and of our capabilities instead.
- KaoruAoiShiho 7y agoDefinitely getting closer, another landmark development just like alphago.
- WnZ39p0Dgydaz1 7y agoAccording to the paper this takes around 53418 hours to train (5e10 frames / 260 frames/sec), distributed over 256 machines that's around 200 total hours wall-clock time.
- odnes 7y agoMind-boggling. They train on each game for a total of 1e11 frames of experience. At 30 FPS that is ~106 calendar years of constant gameplay.
- londons_explore 7y agoThis is how I explain AI to clients... Think of your best employee. You show them how to operate the videoconferencing system, and that's done. Now think of your worst employee. You have to show them how to operate the videoconferencing system 3 times because they keep getting it wrong or not understanding how to start a meeting. Eventually, when they've got the hang of it, they're fine too. AI is like an employee that needs to be shown how to do it a million times. Unless you have the time or the data to show the AI how to do a simple task millions of distinct times, or someone else has already done it, AI can't help you. Here deepmind is just showing that for slightly more complex tasks, that ~1e6 factor turns to ~1e11...
- AndrewKemendo 7y agoI think that's a really cogent explanation and fits a narrative that I've seen work when I try to tell non-technical leaders why they probably aren't ready to do ML/DL.
- antpls 7y agoThat's only the current state of the art however. The goal is transfer learning, meaning that when the AI learns something somewhere, it will improve its knowledge on other taks too. A better analogy would be a baby and an adult. The adult is full of past experiences and can use that experience to learn some other tasks faster. Currently, our AI technology isn't even at the "baby" stage, as it is not able to transfer knowledge between arbitrary tasks. This is an active research domain.
- Tepix 7y agoImpressive! Even more impressive would be outperforming a human with the same amount of training...
- visarga 7y agoThat comparison would be difficult to do. From the time of first experience to beating Agent57 it takes a lot of time to train the human too.
- thedance 7y agoReading the blog post (haven't read the paper yet) makes it sound like this technique might apply to fuzzing. If this thing seeks out and exploits novel states in a large state space, that's the kind of direction you want in your fuzzer too.
- callmekit 7y ago"The human" is an "average human" from the "Human-level control through deep reinforcement learning" paper. For example, for Montezuma's Revenge: - "average human" score is 4753.30 - Agent57 score is 9352.01 - human record is 1219200.0
- deleted 7y ago[deleted]
- deleted 7y ago[deleted]
- francismb 7y agoShouldn't be a comparison between single agent and human performance a better measurement? I mean ok, its defined against "human performance" but a human cannot play parallel on multiple environments or on a tree of possibilities. It just plays again and again single shot/agent.
- maerF0x0 7y agoNot exactly related to Agent57, but to the post itself. Perusing a bunch of those games in the playlist gave me a deep respect for Atari game makers' creativity. Many things that were "games" then may not be considered a game today (eg, a gopher digging dirt to get carrots from a farmer). Also I recognized many similarities between the game mechanics and NES games for example atari road running is like excitebike, pitfall like a boy and his blob, etc.