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There's (a pair of) binary channels that indicate whether the acting player is receiving komi or paying it. (You can also think of this as a "player is black" versus "player is white" indicator, but interpreting it as komi indicators is equivalent and is the natural way you would extend Leela Zero to operate on different komi without having to make any changes to the architecture or input encoding).

In fact, you can set the channels to fractional values strictly between 0 and 1 to see what the model thinks of a board state given reduced komi or no-komi conditions. Leela Zero is not trained on any value other than the 0 or 1 endpoints corresponding to komi +7.5 or komi -7.5 for the acting player, so there is no guarantee that the behavior for fractional values is reasonable, but I recall people found that many of Leela Zero's models do interpolate their output for the fractional values in a not totally unreasonable way! 

If I recall right, it tended to be the smaller models that behaved well, whereas some of the later and larger models behaved totally nonsensically for fractional values. If I'm not mistaken about that being the case, then as a total guess perhaps that's something to do with later and larger models having more degrees of freedom with which to fit/overfit arbitrarily to arbitrarily give rise to non-interpolating behavior in between, and/or having more extreme differences in activations at the end points that constrain the middle less and give it more room to wiggle and do bad non-monotone things.

This is very cool, thanks for this post.

Some remarks:

Playing at an intersection with no liberty is forbidden, unless the play results in capture

This is true, but the capture can be of your own stones. That is, Leela Zero is trained under Tromp-Taylor rules where self-capture is legal. So there isn't any forbidding of moves due to just liberties. Single stone suicide is still illegal, but only by virtue of the fact that self-capture of a single stone would repeat the board position, but you can suicide multiple stones.

However, there is still of course a question of how liberty counting works. See for some past discoveries of positions where Leela Zero is unable to determine when a large group is in atari or would become in atari after certain move(s). This suggests that the neural nets do not learn a general and correct algorithm, instead they learn a bunch of heuristics on top of heuristics that work almost all of the time but have enough combinatorically many nooks and crannies that are not all forced to be correct due to rarity of some of the cases.

Note that if you are going to investigate liberty counting, you should expect that the neural net likely counts a much more fuzzy and intricate concept than literal liberties. As an expert Go player I would expect it almost certainly primarily focuses on concepts that are much more tactically relevant, like "fighting liberties", e.g. how many realistic moves the opponent would need to fill a group accounting for necessary approach moves, recaptures, etc. For example a group with literal 2 liberties but where one liberty was an eye and the other would be self-atari by the opponent unless they made a preparatory connection first would have 3 fighting liberties because actually capturing the group would take 3 moves under realistic play, not 2.

The fighting liberty count is also somewhat multidimensional, because it can vary depending on the particular tactical objective. You might have 4 fighting liberties against a particular group, but 6 against a different group because 2 of the liberties or approach moves required are only "effective" for one objective and not the other. Also purely integer values don't suffice because fighting liberty count can depend on things like a ko.

The fact that this not-entirely-rigorously-defined concept of "fighting liberties" is what expert play in Go cares about (at least, expert human players) perhaps makes it also less surprising why a net might not implement a "correct" and "general" algorithm for liberty counting but might instead end up with a pile of hacks and heuristics that don't always work.

I suspect will be easier to investigate the counting of eyes than liberties, and might suggest to focus on that first if you investigate further. The presence of an eye is much more discrete and less multidimensional than the fighting liberty count, and you only need to count up to 2, so there are fewer behavior patterns in the activations that will need to be distinguished by an analysis.

A particularly interesting application would be to understand Wang et al (2022)’s adversarial policies against KataGo. For example, one of the adversarial policies essentially amounts to confusing the model about the number of liberties that a large circular group has, and capturing it “without the model noticing”. This attack somewhat transfers to Leela Zero as well. Can we find a mechanism for liberty-counting that explains the success of this attack?

See where I give a hypothesis for what the mechanism is, which I think is at a high-level more likely than not to be roughly what's happening.

I also currently believe based some playing around with the nets a long time ago and seeing the same misevaluations across all 4 different independently trained AlphaZero-based agents I tested that the misevaluation probably transfers very well, if not the attack. I.e. if the attack doesn't transfer perfectly, it's probably to do with the happenstance of the preferences of the agent earlier - for example it's probably easier to exploit agents that are trying to also sharply maximize score since they will tend to ignore your moves more and give you more moves in a row to do things - and not because any of these replications anticipate or evaluate the attack itself much better or worse.

However, here again I would strongly recommend investigating eyes, not liberties. All end-to-end observational experimentation with the models I've done suggests that exactly the same misevaluation is happening with respect to eyes. And the analysis of any miscounting of eyes on the predictions should be far crisper than for liberties.

In particular, if you overcount eyes by propagating a partial eye count multiple times around a cycle, you will predict a group is absolutely alive even when it isn't alive, because for groups with 2,3,4,5,... eyes, the data is nearly absolutely consistent that such groups are alive independent of anything else around them. 

By contrast, if you overcount liberties and think a group has a "very large" number of liberties, you might not always predict the group is alive. For example what if every opposing group has 2 eyes and is therefore immortally strong, whereas the large-liberty group is surrounded and has no eyes? The training data probably doesn't so sharply constrain how different nets will generalize to rare "over-large" liberty counts because generally how liberty counts affect statuses is contextual rather than absolute like eye count. So one would expect that when a net mistakenly overcounts liberties, the effect could also be harder to analyze due to being contextual and not always consistent.

Right before the game ends, the value of the board should come down to which player has more territory. Is the model calculating each player’s territory, and if so, how?

I would say almost certainly yes, but possibly not "exactly" territory, maybe something like certainty-adjusted or variance-adjusted territory, with hacks for different kinds of sources of uncertainty. But yes, almost certainly something that correlates very well with territory.

I did some visualizations of activations of a smaller net way back in the past that shows something pretty much like this. 

Although this architecture isn't quite a plain resnet (in particular, this is the visualization of the conv layer output prior to a global pooling layer) , it shows that the neural net learned a concept very recognizable to a Go player as having to do with predicted ownership or territory. And it learned this concept without ever being trained to predict the game outcome or score! In this case, the relevant net was trained solely to predict the next move a human player played in a position.

The fact that this kind of concept can arise automatically from pure move prediction also gives some additional intuitive force for why the original AlphaGoZero work found such a huge improvement from using the same neural net to predict both value and policy. Pure policy prediction is already capable of automatically generating the internal feature you would need to get basic value prediction working.