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Grandmaster-Level Chess Without Search

arxiv.org

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Re: Grandmaster-Level Chess Without Search

#61

Post by janalsncm »

joe_the_user wrote: post #44
janalsncm wrote: post #40

Earlier quoted context omitted.

A sufficiently large nn can learn an arbitrary function, yes. But stockfish is also theoretically perfect given infinite computational resources. What is interesting is performing well under reasonable computational constraints i.e. doing it faster/with fewer flops than stockfish.

Is the model more efficient than Stockfish? I think Stockfish runs on regular CPU computer and I'd guess this " 270M parameter transformer model" requires a GPU but I can't find any reference to efficiency in the paper. Also found in the paper: "While our largest model achieves very good performance, it does not completely close the gap to Stockfish 16". It's actually inferior but they still think it's an interesting…

Well I think it’s interesting to the extent that it optimizes the solution for a different piece of hardware, the TPU. Their results are also applicable to GPUs. Since the problem is highly parallelizable, we might expect a viable model to quickly approximate a more accurate evaluation, and perhaps even make up for it in volume.

Re: Grandmaster-Level Chess Without Search

#62

Post by tromp »

While its performance against humans is very impressive indeed, its performance against engines is somewhat less so:

> Our agent’s aggressive style is highly successful against human opponents and achieves a grandmasterlevel Lichess Elo of 2895. However, we ran another instance of the bot and allowed other engines to play it. Its estimated Elo was far lower, i.e., 2299. Its aggressive playing style does not work as well against engines that are adept at tactical calculations, particularly when there is a tactical refutation to a suboptimal move.

Re: Grandmaster-Level Chess Without Search

#63

Post by janalsncm »

penjelly wrote: post #55

i dont follow... even if its trained anc doesnt use search isnt the act of it deciding the next move a sortof search anyway based off its training? Ive heard people describe LLMs as extremely broad search, basically attempting to build world model and then predicting the next world based on that. Is this fundamentally different from search? Am i wrong in my assumptions here?

We know the model is approximating the results of a search. We don’t know whether it is actually searching.

At the most basic level, the model is just giving probabilities for the next moves, or in the case of value approximation, guessing which bucket the value falls into.

Re: Grandmaster-Level Chess Without Search

#64

Post by chongli »

tromp wrote: post #62

While its performance against humans is very impressive indeed, its performance against engines is somewhat less so: > Our agent’s aggressive style is highly successful against human opponents and achieves a grandmasterlevel Lichess Elo of 2895. However, we ran another instance of the bot and allowed other engines to play it. Its estimated Elo was far lower, i.e., 2299. Its aggressive playing style does not work as w…

I like this even more that I’ve read that. That sounds like it makes this agent a much more human-like player than the perfect calculator traditional chess engines. It may end up being more fun for humans to play against if it’s strong but has holes in its play.

Re: Grandmaster-Level Chess Without Search

#65

Post by ogogmad »

YeGoblynQueenne wrote: post #58
ogogmad wrote: post #57

Earlier quoted context omitted.

The point -- which I don't think you got -- is that extremely generic ingredients like high-quality data (which is the point of Stockfish here) and very deep Transformer-type Neural Networks, are enough to nearly match the performance of ad-hoc, non-generalisable techniques like gametree search algorithms. This has two possible applications: 1. There's far less need to invent techniques like MCTS in the first place.…

I did get the point and I'm commenting that the point is missing the point. There is nothing new in learning that a large neural net can approximate the output of a classical system. This has been done many times before. The real point is that DeepMind build a system that is half-search and pretend it's no-search. You cannot get the "high-quality data" without a classical system- not in chess.

I get your point. Acquiring the data is the hard part, and they cheated to get it. It's chicken and egg indeed.

Re: Grandmaster-Level Chess Without Search

#66

Post by dev_tty01 »

chongli wrote: post #64
tromp wrote: post #62

While its performance against humans is very impressive indeed, its performance against engines is somewhat less so: > Our agent’s aggressive style is highly successful against human opponents and achieves a grandmasterlevel Lichess Elo of 2895. However, we ran another instance of the bot and allowed other engines to play it. Its estimated Elo was far lower, i.e., 2299. Its aggressive playing style does not work as w…

I like this even more that I’ve read that. That sounds like it makes this agent a much more human-like player than the perfect calculator traditional chess engines. It may end up being more fun for humans to play against if it’s strong but has holes in its play.

I suppose. I wonder if one can adjust the engine's skill level.

Re: Grandmaster-Level Chess Without Search

#67

Post by Lerc »

YeGoblynQueenne wrote: post #51
paulddraper wrote: post #18

There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…

>> 1. To score positions in the training data. This is only training data, no search is performed when actually playing. That's like saying you can have eggs without chickens, because when you make an omelette you don't add chickens. It's completely meaningless and a big fat lie to boot. The truth is that the system created by DeepMind consists of two components: a search-based system used to annotate a dataset of mo…

It doesn't matter where the egg came from, just that it is an egg.

It could have luckily coalesced from gas (a Boltzmann egg), or perhaps even more radically, been laid by a duck.

you say

>They didn't train their system by self-play and they certainly didn't hire an army of low-paid workers to annotate moves for them.

So you are certainly aware that there are avenues to creating the data set. Given that, it is quite reasonable to say that search is unnecessary.

Re: Grandmaster-Level Chess Without Search

#68

Post by themoonisachees »

IIAOPSW wrote: post #26

Slightly off topic but am I the only one that approaches strategy games by making a "zeroth order approximation". Eg find the shortest path to victory under the (obviously faulty) assumption that my opponent does nothing and the board is unchanging except for my moves. Now find my opponents shortest path to victory under the same assumption. Then evaluate, if we both just ignore each other and try to bum rush the vic…

This is one of the canonical ways people learn chess. It's not that bad of a way to play because it emphasizes thinking about good moves, and it efficiently finds mates in N (when done by a human)

In higher level play it usually loses to opponents that are aware they're not playing alone, at least that's the case with bots that do in fact stay unaware of their opponent.

Re: Grandmaster-Level Chess Without Search

#69

Post by devit »

janalsncm wrote: post #38
famouswaffles wrote: post #13

Well without explicit search would probably be more accurate. They note that though in the paper: >Since transformers may learn to roll out iterative computation (which arises in search) across layers, deeper networks may hold the potential for deeper unrolls.

We don’t know if it’s using implicit search either. While it would be interesting if the network was doing some internal search, it’s also possible it has just memorized the evaluations from 10M games and is performing some function of the similarity of the input to those previously seen.

Maybe they could test it on "tricky" positions where increasing the search depth on Stockfish dramatically changes the evaluation.

Re: Grandmaster-Level Chess Without Search

#70

Post by pixelpoet »

xbpx wrote: post #30
IIAOPSW wrote: post #26

Slightly off topic but am I the only one that approaches strategy games by making a "zeroth order approximation". Eg find the shortest path to victory under the (obviously faulty) assumption that my opponent does nothing and the board is unchanging except for my moves. Now find my opponents shortest path to victory under the same assumption. Then evaluate, if we both just ignore each other and try to bum rush the vic…

That's why white has a higher statistical win rate ya?

Only if you play on the Tengen point.
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