bini0823: how to make clash of code game?
bini0823: I find
awesomeinsan: hello
awesomeinsan: can anyone help me with robot show puzzle solving ?
awesomeinsan: i need some hints
DaNinja: click Forum on the left for hints
DaNinja: its easier than it seems at first
DaNinja: draw each step on paper then do what Stan Lucian suggests
dbdr: nice progress Marchete
Marchete: not a lot
Marchete: but it's good to have an alternative UCT formula
Marchete: I'm training with AZ
Marchete: but on arena I'm testing the new
Marchete: I tried to add regularization to limit weights range
Marchete: PolValKernel:-2.6428108/1.7513872/-0.01126709
Marchete: higher range than I expected
Marchete: I wanted to try some kind of quantization
Marchete: but on an unorthodox wayç
float: he;;p wpr;d
float: hello world*
Marchete: :wave:
float: brb game
martinpapa69: adding weight regulazation usually helps. but i think it has bigger effect on bigger networks
Marchete: regularization is an indirect way I think I have to limit weights a bit
Marchete: not only for avoiding unnecesary model complexity
Marchete: but -2.64 .. 1.75 range is higher than I liked
martinpapa69: oh sure. you want to compress your net to 8bit ?
Marchete: I wanted to test 16 bit first
Marchete: I want to check all max values per range and kernel/bias
Marchete: then do -32765..32765 per layer
dbdr: regularization would slow down inference, no?
Marchete: inference?
Marchete: in fact it doesn't appear
Marchete: L2 regularization for example I think only goes on losses
martinpapa69: it only effects the training
Marchete: i.e. weight values add loss
Marchete: then TF add it to total loss
Marchete: and train with that
dbdr: right, that one :)
Marchete: it's some weird way to say "don't overthink model complexity"
Marchete: and batch normalization layers are "similar"
Marchete: but they do have weights
Marchete: but in the end
Marchete: they are just tweaks to previous kernel+bias
Marchete: so on SaveModel you can "mix" all
Marchete: and you don't need a real batchnorm layer on inference
dbdr: i see
Marchete: and doesn't affect inference performance
dbdr: tf does the mixing automatically?
Marchete: I'm not using it now
Marchete: but it's kind of like that
Marchete: def SaveModel(my_model,fileSTR,debugPrint=False): http://chat.codingame.com/pastebin/ce9f8552-a1b2-4ee7-8f81-88d2bebbc15f
Marchete: not TF
Marchete: myself with pen and paper :D
Marchete: watch out
Marchete: that SaveModel has policy+value concatenation
Marchete: if the model isn't as expected it will explode
Marchete: it's not for general use
Marchete: gamma beta moving_variance, etc
Marchete: are from a BatchNorm layer just between Dense and Relu layers
Marchete: Dense -> BatchNorm -> Relu
Marchete: good thing -> it's free from inference PoV
Marchete: bad thing -> more complexity, and probably L2 regularization does similarly
dbdr: the concatenation only saves one avx iteration on the output layer, right? is it worth it?
Marchete: shrugs
Marchete: one avx iteration is one iteration
dbdr: only proportions matter
dbdr: if it's one in 1000 it's noise
Marchete: first layer is cheap thanks to one-hots
Marchete: I need to reprofile
Marchete: I wanted to try quantization, robo used int8 and it was much faster than int16
Marchete: int16 was 20%, int8 a 50%
Marchete: but I was expecting 80-100% improvement in int16
Marchete: half size
Marchete: ah, also this SaveModel adds layer counts at start
Marchete: because this way I don't need to manually add it on code
dbdr: yes, strange that speeds are not bigger
martinpapa69: Marchete BN layer is not included in your published inference source?
Marchete: Batch Normalization?
martinpapa69: yes
Marchete: as I said
Marchete: if BN is after a dense in inference is just a tweak to weights + bias
Marchete: but no, I haven't it in published either
martinpapa69: ah okay. and tf does this merge automagically?
Marchete: nope
martinpapa69: aight
Marchete: if ('/kernel' in x.name): http://chat.codingame.com/pastebin/d82e7f93-7b1b-4f17-a7e0-3d530a4a06e8
Marchete: its that part
Marchete: I even pasted my "hacked" SaveModel just 5 min ago
Marchete: hacked as with some header data
Marchete: about layers and neuron count
martinpapa69: ah okay, i failed to scroll up two pages
Marchete: TLDR: gamma,beta,moving_mean,etc are used to change weight and bias
Marchete: and I think it's correctly done
Marchete: so inference wise, both BN and L2 regularization are free/cost 0
Marchete: the latter seems simpler
dbdr: isn't L2 just to keep weights small?
dbdr: BN is supposed to speed up training too
martinpapa69: L2 is too prevent neurons to be too dominant in a layer. it can help generalization
martinpapa69: and also it prevets float overflow
martinpapa69: dunno if it speeds up training
Marchete: BN in theory let's you use higher learning rates
Marchete: so learns faster
Marchete: I did a single test
Marchete: maybe bugged
Marchete: and it wasn't that good
martinpapa69: its interesting that there is a point in every training, where the actual state of the nn plays a lots of draws in the actual submitted code
martinpapa69: it looks like there are not many different directions in the training
Marchete: yeah with some cpuct and after some point
Marchete: diversity is lower
Marchete: just try to add more random
Marchete: like on pickBest
Marchete: more turns and more random percentage
Marchete: it shouldn't affect sample quality
Marchete: I went from 60% winrate to 37% in the next gen
martinpapa69: ye i noticed these random noice stuff rly help the training. the result is much better if the TRAIN_NOISE_RANDOM is a bit higher
Marchete: and it hardly goes over 44% after 7 gens...
Marchete: it is?
Marchete: If I recall that noise is applied to NN value eval
martinpapa69: if i set TRAIN_NOISE_RANDOM to something like 0.001 i never get good result
Marchete: so it could affect samples more
Marchete: but good to know
Marchete: that noise comes from jacekmax
Marchete: jace_k applied some +-0.0x% to his NN values
martinpapa69: i dont like, it because it looks a bit hacky for me, but it rly helps
Marchete: like what value you seem it's working?
Marchete: I also barely use it
martinpapa69: I use the value that you commented after the line (0.03)
Marchete: I have like 0.03 rn
Marchete: ahh
Marchete: ok
Marchete: that's only 0.97..1.03
Marchete: I always used that value so I haven't tested more :D
martinpapa69: ye, but I was about to completly remove that noise, but apparently i cant
Marchete: maybe it's a bug on my code
martinpapa69: i thought the noise added by the exploration param (cpuct?) should be enought. ye it could be a bug idk
Marchete: the noise from dirichlet
Marchete: cpuct isn't noisy
martinpapa69: oh, the dirichlet noise, ye.
Marchete: if you set both dirichlet and TRAIN_NOISE to 0
Marchete: it should be deterministic
Marchete: ah not
Marchete: I added in selfplay more diversity at pickBest
dbdr: hardcoded :)
Marchete: yeah
Marchete: it shouldn't
dbdr: WE WANT MORE HYPERPARAMETERS
martinpapa69: you mean this stuff:
martinpapa69: int randomPick = rnd.NextInt(rootNode->ChildCount);
dbdr: yes
Marchete: no
Marchete: the prior one
Marchete: rnd.NextInt(1000) < XX
Marchete: just before picking that line
Marchete: that can be improved
Marchete: like if ChildCount ==1 don't try to change
Marchete: and also move the random pick before the normal selection, and check if randomPick it's the same as selected
martinpapa69: yee it looks a bit hacky too :D did it improve the result a lot ?
Marchete: in that case don't do anything, don't invalidate samples
Marchete: it gives more diversity, yes
dbdr: if ChildCount == 1 it's harmless, no?
Marchete: it is
dbdr: apart from losing the samples
Marchete: but can invalidate samples
Marchete: so it's undesired
dbdr: yeah
Marchete: maybe I'm finicky on these
dbdr: it's quite standard, epsilon-greedy to add variety
dbdr: so not so hacky I say
Marchete: neither me
Marchete: the hack it's maybe ignore these samples
Marchete: gotta go, cya!
dbdr: bye!
martinpapa69: bb
derjack: epsilon-greedy is meh
martinpapa69: ye, i dont like it either
Marchete: what alternative then?
Marchete: e-greedy is simple and gives exploration, sampling discarding also avoids bad samples
Marchete: it's a win-win
martinpapa69: ye, idk but it feels hacky. same with the temperature used in AZ
Marchete: temperature without some epsilon would need thousands of matches to cancel out all that noise
Marchete: if they just pick a random weighted sample in all cases
Marchete: that's a lot of noise
martinpapa69: i like how the give it the letter τ, so it sounds more scientific
Marchete: https://arxiv.org/pdf/2012.11045.pdf
Marchete: section 5.2
Marchete: my a** pulled random has a name, e-greedy and it's an improvement to avoid local optima
derjack: 5.1 talks about mcts solver?
Marchete: also "However, in practice, we see that a lot of the times the
fully deterministic PUCT algorithm from AlphaZero needs
an unpractical amount of simulations to revisit optimal actions where the value evaluations of the first visits are misleading."
Marchete: probably
martinpapa69: ye sure, i understand it's necessary, and can help the training, but i still dont like it
Marchete: "Following this regime, we propose to use disconnected trajectories for e-greedy exploration. In other words, we intentionally create an information leak."
Marchete: that's like my sample discard on parents
Marchete: so you may not like it
martinpapa69: :D
Marchete: but it has some reference in formal papers
Marchete: ;)
martinpapa69: i didnt check that part yet
dbdr: why is it information leak?
Marchete: reading it they like disconnect the graph when greedy
Marchete: if the mechanism is utilized at nodes deeper in the tree, we disregard the value expectation formalism and corrupt all its parent nodes on its trajectory.
Marchete: I think it's similar
Marchete: see graphs at page 9
Marchete: e-greedy alone gives +100ELO
Marchete: so it's a good thing to have it
Marchete: either that or have 5000TPUs and do like 3-years train in 3 days
dbdr: yes, I always cringe at the "learned chess in 4 hours"
dbdr: push Marchete to #2, martinpapa69 ;)
Marchete: \o/
Marchete: only 0.07
martinpapa69: haha, anything can happen. i submit a lot
dbdr: what's your TRAINING_POOL_SIZE? I have 800K, and it seems if I increase it further I have memory issues
dbdr: maybe I need to try the minibatch feature
martinpapa69: would be nice to have a copy of these multis, just to throw your ais, to test them
dbdr: sorry, I mean TRAINING_SUBSET_SIZE
dbdr: you can create a contribution with the same code
martinpapa69: TRAINING_SUBSET_SIZEis not used in my code, i just keep the last 15 raw selfplay result, without using the NNSampler
dbdr: well, how many samples is that?
Marchete: with 16GB I use 850k, no problems
martinpapa69: i dont know i dont count it. its size is 400MB on the disk :D
Marchete: just a lot of gc.collect
Marchete: I don't know how to properly do the minibatch
dbdr: 1.1GB here
martinpapa69: its the size of the samples.dat, right ?
dbdr: yes
dbdr: for 800K
Marchete: with sample deduplication
martinpapa69: there you go Marchete
dbdr: :+1:
Marchete: :D
Marchete: it was //v24 gen0054
dbdr: btw, dedup is nice, but a sample that comes up a lot might be more important to learn
Marchete: guess how many versions and generations :rofl:
Marchete: sample_weight in model.fit
Marchete: if you really want to
dbdr: aha, interesting
Marchete: maybe limit the upper bound
Marchete: 1..10 or so
martinpapa69: dbdr, thats what i was thinking about. no idea if its worth it to pack duplications
Marchete: or whatever that sample_weight parameter needs
Marchete: disk and memory wise, hell yes
dbdr: if you care about disk...
Marchete: as at some point used /run/shm memdisk, yes
Marchete: a lot
Marchete: and it was much faster
dbdr: storing 1 hots as float32 is not super efficient ;)
Marchete: I know
Marchete: if you ever zip samples
Marchete: they have like 95% compression
dbdr: a simple gzip divides size by 30 :)
Marchete: I thought about that, zipping samples
Marchete: python is easy
Marchete: as always
Marchete: c++....
dbdr: there must be a lib
dbdr: or system("gzip ...") ;)
Marchete: system gzip destroys the purpose to reduce IO
Marchete: and c++ libs are like
Marchete: well, like c++ libs are
dbdr: :D
Marchete: I just want oneliner memory zipper
Marchete: like python C# etc
dbdr: print the samples to stdout, pipe to gzip
derjack: or write one hots more efficiently
derjack: i have only 14 integers as input
martinpapa69: maybe you could add a one-hot converter layer to the nn
martinpapa69: so yo ucan store the input as integer
Marchete: I wanted to keep the sample agnostic
Marchete: all values as floats
Marchete: I think it's simpler to add a zip layer
Marchete: than any other tweak
Marchete: and it's more transparent
Marchete: selfplay->zip->sampler->zip->tensorflow
Marchete: low IO, no weird one-hot exception
derjack: and during learning you dont use any fancy sparse vectors or something?
Marchete: beg your pardon?
Marchete: what's sparse vector?
derjack: dunno how its called exactly
dbdr: a holy vector ;)
Marchete: ah yes
Marchete: googled it
derjack: but as you can exploit one hots to being more efficient, the same you can do for learning
derjack: no need to multiply all those zeroes
Marchete: I don't multiply on C++
Marchete: on TF I don't care
dbdr: yeah, most time is spent on selfplay
martinpapa69: btw you can add regularization to the layers in the layer declaration like
martinpapa69: x = tf.keras.layers.Dense(69,activation='relu',name='Dense1', kernel_regularizer=regularizers.l2(0.01))(inputs)
Marchete: I know, I'm doing it rn
martinpapa69: ah okay :d
Marchete: but I went all in
martinpapa69: i tried to add it to the loss func
martinpapa69: but its more simple
Marchete: x=tf.keras.layers.Dense(96,name='Dense1',kernel_regularizer=tf.keras.regularizers.l1_l2(l1=L2_REGULARIZATION/10.0, l2=L2_REGULARIZATION),bias_regularizer=tf.keras.regularizers.l2(L2_REGULARIZATION/10.0))(inputs)
Marchete: lol
Marchete: like all to red!
martinpapa69: ahh :D
Marchete: it's a buffet meal
Marchete: free? add it!
Marchete: :hamburger::taco:
Marchete: martinpapa69
Marchete: I think you don't need to manually change the loss
Marchete: model.fit auto add it, it seems
Marchete: so regularizers are just these configs, nothing more
martinpapa69: ye, i hope is it works automatically
martinpapa69: even though it wont show up in the model.fit() log
martinpapa69: like
martinpapa69: 4511/4511 - 7s - loss: 0.4414 - value_loss: 0.1847 - policy_loss: 0.2567 - value_mean_absolute_percentage_error: 244826.9688 - policy_categorical_accuracy: 0.8383
dbdr: this is just changing the loss, so no other change is needed?
martinpapa69: but ye if add the policy and value loss, its smaller than the summ :D
Marchete: https://github.com/keras-team/keras/blob/master/keras/engine/training.py#L749-L752
dbdr: value_mean_absolute_percentage_error is garbage
Marchete: no loss in change
Marchete: no change in loss :D
Marchete: I think model.fit auto adds it
Marchete: on the other hand, if you do your training loop manually (I have no idea how), you must add it
dbdr: I mean, model saving and moka don't need changes?
martinpapa69: regularization dont add new weights to the nn
Marchete: l2 regularization affects how weights + bias change on training
dbdr: ok, thx
Marchete: it isn't any inference change
Marchete: so it's free use
Marchete: no change in Mokka/c++, neither on loss calculation
Marchete: only declaration on layers
dbdr: high level problem: arena is RPS, selfpit is not always meaningful
dbdr: how do we measure progress?
martinpapa69: you need to submit your code twice an hour
Marchete: :D
dbdr: RPS
Marchete: while (!first()) { resubmit(); }
dbdr: robo is another league tho
dbdr: so that's an infinite loop ;)
Marchete: make slight changes to cpuct
Marchete: resubmit more
Marchete: also my cpuct on selfplay is different from arena
Marchete: I tweak it
Marchete: in fact right now it's a completely different formula
Marchete: I think in selfplay I prefere more exploration than in arena
dbdr: makes some sense
justin-tse: Hello, world! How to better understand and use javascript's higher-order function?
Could anyone give me some suggestions?
chop-chop: @justin-tse official react documentation has a good article about HOC
chop-chop: * react.js
justin-tse: @chop-chop thank you!
Thyl: Hi !
SamOrton: hai, i just got this >.< um im only 15 and wanna start coding and i dont know where to start
martinpapa69: write a sneak game with console visualizer
SamOrton: whats that?
martinpapa69: https://www.youtube.com/watch?v=KcpRyIFU7Eg
Saunuri: freecodecamp has plenty of beginner friendly tutorials
SamOrton: OH SNAKE! i know this yes. you put sneak sorry so i was like wait what haha
Saunuri: I'd suggest first thinking about what you want to do and then picking a language
SamOrton: which is the easiest for me as a teen who is only learning computing at school so far? I saw its like Python? (when im older ill obiously try study java at university etc)
Saunuri: Python is a good starting point but I don
Thyl: Python is very good for starting and coding
Saunuri: I think you can still go with C# or javascript if you want to
SamOrton: okay thank you :D its just all confusing because this website is abit too hard for my small brain ha
Saunuri: The tutorials are free so you can get a feel for which languages you feel comfortable with
SamOrton: okay thank you :D
derjack: :upside_down:
SamOrton: :>
Marchete: https://github.com/lightvector/KataGo -> "If tuned well, a training run using only a single top-end consumer GPU could possibly train a bot from scratch to superhuman strength within a few months."
Marchete: le what?
Marchete: a few months if tuned well?
Marchete: ohmy
RoboStac: go's very complicated
derjack: well, 6 years ago computer go was very far from professional players
derjack: and its again very far, but from opposite direction
derjack: and 5000TPU in days -> 1 gpu in few months is progress
derjack: hmm https://arxiv.org/pdf/2105.06136.pdf
derjack: so they use vanilla mcts moves as expert in first iterations?
derjack: well not only vanilla, but rave etc
RoboStac: they do mention having good smaller networks as well but the site seems to be down so can't tell how long they trained for
Marchete: my trainer keeps giving me best models, and all are worse on arena...
dbdr: same here
derjack: lets breed those models for GA
Marchete: I think it just ends in the overfitting area
RoboStac: from your discussions earlier I think your sample counts may just be really low
dbdr: yes, and finding the magic weights that happen to trip up the arena
Marchete: it's probably low
Marchete: but I don't want to go to the "500k matches"
derjack: for oware i had buffer of 12m positions
Marchete: lol dbdr
Marchete: so true
RoboStac: my current bot was 600k matches
Marchete: arena is deterministic
Marchete: so a 0.03 change is +100% winrate vs some
Marchete: 600k matches in total?
RoboStac: yeah
dbdr: how big buffer robo?
Marchete: 72 gen * 2k matches = 144k
Marchete: 600k is a lot of time...
RoboStac: I think 15m samples (may have been 10, can't remember if that change was before or after)
Marchete: but many of these are discarded at some time, no?
RoboStac: yeah, it's a rolling queue of that many samples, oldest get discarded
Marchete: but a queue of 500k?!
Marchete: 500k matches?
RoboStac: ?
Marchete: or how do you know it's 500k matches
RoboStac: 150 trainining iterations
RoboStac: of 4000 self play matches
Marchete: hmm
Marchete: I do like 2.5k per iteration/generation
RoboStac: (I make snapshots every 50 iterations to test in arena / benchmark)
Marchete: ahh ok
Marchete: you go more like AZ, I messed up with AlphaGoZero
Marchete: so I use it differently
dbdr: so 3 snapshots?
RoboStac: well, I did let it go to 200 iterations but it didn't seem to be much stronger (though it's not obvious to tell)
dbdr: or 3rd was best
dbdr: yeah
derjack: only cool kids ignore pits
Marchete: they don't pit
RoboStac: I don't do any sort of winrate checking between my own versions
derjack: :+1:
Marchete: but I saw sometimes a drop to 33% winrate vs best
derjack: but, did you test it in leaderboard anyway?
RoboStac: yeah, but is that reflected in actual results against other people or is it an overfitting issue?
Marchete: I see more overfitting issues by always playing vs itself
Marchete: instead of pitting vs 2 bests
dbdr: the thing is, if you have a long replay buffer, you are not overfitting
Marchete: define long
dbdr: long enough ;)
Marchete: I'm doing 800k right now
prenisbones: fuck
dbdr: like robo's 10/15M
Marchete: so I can't overfit even if I use previous samples
Marchete: or 800k is low?
derjack: 800k samples?
dbdr: robo's is much bigger
Marchete: yes
RoboStac: I'd say low, but not sure how dedup / weighting affects it
thoustgenitals: FUCK
derjack: i have 12m
thoustgenitals: i have a 12m cock
Marchete: it doesn't explode on TF?
Creedo: ok
RoboStac: I'm not learning off all of them each iteration
Marchete: minibatches I guess
Creedo: Im the loudest on discord
Marchete: take some small subset and all that
derjack: and i dont use libs ~
BlaiseEbuth: dbdr saved him, I banned
derjack: oO
dbdr: all handmade derjack?
RoboStac: I did struggle a lot with python memory usage though, was stuck at about 3m
derjack: dbdr yes
Marchete: well, I'll investigate that part
RoboStac: my none python sample history is 15m and uses less memory
Marchete: I'm more confident with C++ about memory management
Marchete: I have no idea what's python doing
RoboStac: yeah
dbdr: copying all over the place
prenisbones: guys i cum red
prenisbones: lkasnajdad
derjack: BlaiseEbuth
prenisbones: i hack into blockchain
prenisbones: kpodskfop\d
prenisbones: =0dskspl\
BlaiseEbuth: (╯°□°）╯︵ ┻━┻
dbdr: \o/
Creedo: ok
sausaugechomper: dbdr is a massive fucking nerd
sausaugechomper: nerd
sausaugechomper: nerd
sausaugechomper: nerd
Creedo: a
Creedo: ok
BigD398: can't dissagree
BigD398: blaiseebuth is also a nerd
BigD398: quit it with the dumb faces
BlaiseEbuth: Others? Say it now, I'll gonna eat after that.
derjack: Automaton2000 what do you think
Automaton2000: i was talking to you
donbenarfa: wus poppin b
Mnky: ayy my slime
srtewc: fuck ur mom
Mnky: Same
srtewc: :rage:
dan_w_meatball69: 👽
Creedo: ok
Creedo: http://chat.codingame.com/pastebin/5f8f8d92-c71e-4c1a-bed1-0c869e16ad1f
Creedo: http://chat.codingame.com/pastebin/1b2fd06f-bf05-4102-aca3-3c31073727ce
Bundajcheeks: :ok_hand_tone2:
brokenfingerman: hi
dan_w_meatball69: by the rivers of babylon
dan_w_meatball69: where we sat down
dan_w_meatball69: yeah we wept
AFUFAFU: yeeeeeee
KiwiTae: there is a kid class ongoing?
Astrobytes: Was
Astrobytes: Most were kicked/banned
KiwiTae: o/
Astrobytes: greetings :)
dbdr: \o Astrobytes
Awanpro: hai
Awanpro: i'm new user
Astrobytes: dbdr :wave:
Astrobytes: hello new user
sadaecho: this is a great site
sadaecho: long time ago I was looking for stuff like this
sadaecho: glad I found it
Astrobytes: Enjoy!
derjack: this site is something more than clashes
Astrobytes: don't be sillly
derjack: silly filly
Astrobytes: ...
derjack: woah nice jump
Astrobytes: hm?
derjack: i'd say, a breakthrough in ranking eh
Astrobytes: Meh. Slight improvement, not much. Was 22nd last night
Astrobytes: Just been experimenting today
Astrobytes: Bot isn't consistent enough.
Astrobytes: Definite improvement since I added null move pruning though.
derjack: null move? but breakthrough is so blitzkrieg
derjack: no blitzkrieg... the other german word
Astrobytes: zugzwang?
derjack: yeah
Astrobytes: Yet it works
derjack: or maybe you have some sign bug, and it canceled out
Astrobytes: No sign bugs as far as I can tell.
darkhorse64: you have transposition tables too, I guess ?
Astrobytes: yep, replace if >=
Astrobytes: *depth
Astrobytes: Tried to get tric's trick working but the value of k eludes me currently
derjack: tric trick?
darkhorse64: ^
Astrobytes: Yes, it's a secret.
derjack: have you tried trac track?
Astrobytes: Oh. That must be it...
nulte: astro keeps bullying my bot
Astrobytes: sorry :)
NgonTran: hello
NgonTran: hello guys
Skinjbir: hello
AllYourTrees: whats a null move Astrobytes
AllYourTrees: :eyes:
Art3m1s_F0w1: unrelated, but does anyone use Java for clash of code?
BlaiseEbuth: That combo...
Astrobytes: AllYourTrees: sorry was afk https://www.chessprogramming.org/Null_Move_Pruning
AllYourTrees: interesting ty!
Astrobytes: np
Marchete: [..] with Chrilly Donninger, who popularized null move by his Null Move and Deep Search paper in the ICCA Journal 1993
Marchete: it's from 1966
Marchete: :D
21stCenturyPeon: @Art3m1s_F0w1 I do, but I'm old and set in my ways. Naturally, it's terrible for Shortest mode
AllYourTrees: do they have language specific leaderboards for shortest mode?
AllYourTrees: also is there a way to only do shortest mode in CoC?
BlaiseEbuth: No language lb in coc
BlaiseEbuth: And yes, but only in private clashs
Art3m1s_F0w1: yeah Java is the only language I know well enough to do speed challenges in but I figured it was a terrible choice
jacek: python or ruby are faster to write
jacek: if you know only java, why bother with clash of codes at all :v
BlaiseEbuth: Why bother wirh clash of codes?
Art3m1s_F0w1: idk its fun
Electroxd988: hi
jacek: oO
Marchete: :O
Wontonimo: hey Art3m1s_F0w1 Electroxd988 , try some of the challenges like https://www.codingame.com/training/easy/mars-lander-episode-1
Wontonimo: warning - turn off your volume
Wontonimo: 21stCenturyPeon you may enjoy this bot battle https://www.codingame.com/multiplayer/bot-programming/tic-tac-toe or this one https://www.codingame.com/multiplayer/bot-programming/connect-4
nulte: o.o
nulte: https://www.codingame.com/replay/570532520
jacek: Oo
Wirmi: how can i put debugger mode?
Wirmi: or set
jacek: you can write to stderr
nulte: hmm, I wonder if ept will help
eulerscheZahl: why turn down the volume? there's nothing more relaxing than https://www.codingame.com/servlet/fileservlet?id=955564061908
nulte: (╯°□°）╯︵ ┻━┻
nulte: euler your bt bot is minimax?
eulerscheZahl: i think so
eulerscheZahl: but not looking at the full board at once
eulerscheZahl: minimaxing on smaller regions
nulte: hmm
nulte: Maybe ill try pruning more moves after I add EPT
nulte: Currently i only prune moves if they are forced moves
eulerscheZahl: forced moves? this is not checkers
nulte: if opponent has a piece in 7th rank
nulte: its a forced capture
jacek: what if both you and opponent have piece on 7th and its your turn?
nulte: then i win
grateful_tomato: what crates are available in Rust? I couldn't find a list, but e.g. "use time" works
jacek: crates?
grateful_tomato: that's how Rust calls packages
grateful_tomato: like Python Numpy
jacek: oh
grateful_tomato: would be also interesting to know what's available in Python and Ruby :-)
grateful_tomato: ok, found it: https://www.codingame.com/faq
grateful_tomato: Rust 1.51.0
Includes chrono 0.4.19, itertools 0.10.0, libc 0.2.93, rand 0.8.3, regex 1.4.5, time 0.2.26
Electroxd988: hi
jacek: good evening
Wirmi: The game is not showing img, does anyone know why?
nulte: which game?
Wirmi: coders strike back
jacek: reload the page
nulte: have you tried to refresh?
Wirmi: yes but 1m later, again
Wirmi: im using opera, this may be the reason
jacek: :thinking:
Wirmi: np, thanks for the help :)
Art3m1s_F0w1: opera probably isnt fully supported though ogx hasnt given me a problem so idk
jacek: oO
ramankala: why are the solutions to the classic puzzles locked?
Astrobytes: You have to solve them first in that language. A contentious feature.
Astrobytes: Locking all solutions before you solve it at all is fine though, don't want you cheating eh
ramankala: gotcha
nulte: my mcts is writing in memory that it shouldnt :/
AllYourTrees: :scream:
AllYourTrees: bad mcts!
Astrobytes: reusing nodes?
AllYourTrees: you need to keep your mcts on a shorter leash :)
nulte: im reusing yes
nulte: but its overwriting a var that isnt even used in mcts
Astrobytes: lol what
nulte: yep
nulte: Dont know how it happens
nulte: https://www.codingame.com/replay/570543394
nulte: here i make an illegal move
nulte: should have played f2e3
nulte: but it rotates it and it prints that
nulte: because the player_id has been overwritten
Astrobytes: I saw you had this issue yesterday, I posted a replay but I think you were offline
nulte: strange bug
Astrobytes: You debugged it in VS?
nulte: it didnt happen
nulte: with the same gamestate at least
nulte: maybe when i reuse it causes a bug
Astrobytes: Yeah, I'd look there first
Astrobytes: forgetting to reset the parent node? Surely not that
Astrobytes: or next node rather
jacek: or initialization problem?
nulte: maybe
nulte: It doesnt happen a lot
Astrobytes: Definitely the hallmark of node reuse imo
nulte: I might have a bug in c4 then since the mcts code is the same
nulte: o.o
**Astrobytes wonders why we're helping here...
nulte: my bot is still far from the goal
jacek: for humanity advancement?
Astrobytes: catgirls obviously
nulte: oh no
nulte: I thought i fixed it with a hack
nulte: but still happens
nulte: (╯°□°）╯︵ ┻━┻
Astrobytes: often the nature of hacky fixes ;)
jacek: use fixy hacks
Astrobytes: :thinking:
jacek: happy Caturday
nulte: I think i was writing outside of array
jacek: thinking outside of the box eh
Diptastic: im assuming test06 no temperature on temperatures challenge is broken...
nulte: dont think so
Diptastic: http://chat.codingame.com/pastebin/cdf2028b-81f8-4efb-a4bc-92ba00db0492
nulte: are you printing "0" or 0
Diptastic: it doesnt matter, in c# 0 gets converted to string in console write line
Diptastic: http://chat.codingame.com/pastebin/9098a045-e606-432e-9d55-0d225b19b313
nulte: ah
nulte: you are outputing 0 twice
nulte: if (tempnumbq == 0) {
Console.WriteLine(0);
return;
}
Diptastic: that is just checking if targetTemp is the default value and has not been set to a potential target value.
if an actual value is 0 in the array then the first if clause above that will do as you imply in the block :)
Diptastic: My block is returning 0 as apporpiate
Diptastic: but the unit test fails, says it expects nothing
Diptastic: so when I log an empty console.writeline then it fails also saying it found nothing but expects "0"
nulte: yes
nulte: but your code keeps runnig
nulte: which makes it output twice
Diptastic: to break this sol down.
line 36
Diptastic: line 20
Diptastic: oh ignore me
Diptastic: I just guessed that when you write console.writeline the test resolves
Diptastic: thanks!
nulte: np
MorningStar37: yw
KiwiTae: nulte o/
nulte: hi kiwi
KiwiTae: helping the newbies? :D
nulte: it was a simple question and he pasted the code
KiwiTae: hehe
nulte: I still need to figure on what to do next on breakthrough
KiwiTae: Astrobytes <= this person never sleeps Oo
KiwiTae: breakthorugh? is that a puzzle?
nulte: board game
nulte: its a multi
nulte: https://www.codingame.com/multiplayer/bot-programming/breakthrough
KiwiTae: ooh havent check that one
nulte: simple rules
nulte: get to 8th rank
KiwiTae: gjob
KiwiTae: oh worth up to 2k cgpts
Astrobytes: never sleeps eh
KiwiTae: i just finished work >< should I begin on HS or go to bed?
KiwiTae: zzz ~ see ya in a couple hours
nulte: cya
KiwiTae: good luck on breakthrough
nulte: thanks
Astrobytes: very quick decision there Kiwi :D
KiwiTae: you better be top 10 when i come back ><
nulte: gap from 20 to 10 is massive
Astrobytes: Don't encourage him!
Astrobytes: It really is
nulte: I think ept might improve my bot though
nulte: it still does rollouts until game ends
Astrobytes: I'm thinking I may have to switch from my alphabeta to ept, I am not trictrac unfortunately
nulte: the problem with EPT is that I need an eval :(
Astrobytes: Heh
nulte: I feel like minimax is harder to implement
nulte: at least if you add features
Astrobytes: Yeah but I kinda like that
nulte: is tric the only one using minimax?
nulte: in top 10
Astrobytes: Looking at the list I think so
Astrobytes: Eric perhaps
Necromonkey: 0
ja_fica: I have lots of entries that require 95k code size...
ja_fica: Im using 64bits numbers
ja_fica: how to convert it to base 65535?
MiyamuraIzumi: what is EPT ?
ja_fica: early playout termination, i think
Zenoscave: nulte what do you use?
bini0823: how to make clash of code game?
