AlphaGo Zero And AlphaZero, RomiChess done better

[Michael Sherwin]

They can say or omit anything. One article definitely stated that a database of human games were loaded. I don't know what article is correct. I just know from years of experience with that type of learning that the moves in the games by AlphaZero against Stockfish has the same type and feel. Take what I said with a grain of salt but take what is said in those articles with a grain of salt as well.

Edit: Think about it in these terms. It took 4 hours for AlphaZero to amass all human knowledge about the game of chess. At one minute per move maybe 4 games of self play could have been accomplished. However, if 6 million games were loaded in and analysed then 4 hours is about the time it would take. Self play would take years to get to that level. 'Their' story does not add up!

[kranium]

They can say or omit anything. One article definitely stated that a database of human games were loaded. I don't know what article is correct. I just know from years of experience with that type of learning that the moves in the games by AlphaZero against Stockfish has the same type and feel. Take what I said with a grain of salt but take what is said in those articles with a grain of salt as well.

Edit: Think about it in these terms. It took 4 hours for AlphaZero to amass all human knowledge about the game of chess. At one minute per move maybe 4 games of self play could have been accomplished. However, if 6 million games were loaded in and analysed then 4 hours is about the time it would take. Self play would take years to get to that level. 'Their' story does not add up!

No human games were loaded. Learning was accomplished thru millions of self-play games
The monte carlo search algorithm simply chose the move in each position with the highest win probability.

Code: Select all

Mini-batches 700k 700k 700k
Training Time 9h 12h 34h
Training Games 44 million 24 million 21 million
Thinking Time 800 sims 800 sims 800 sims
40 ms 80 ms 200 ms

Table S3: Selected statistics of AlphaZero training in Chess, Shogi and Go

They used 5,000 first-generation TPUs to generate self-play games.
and 64 second-generation TPUs to train the neural networks.

They ended up with 44 million training games.

[kranium]

There's a version of AlphaZero available here:
https://github.com/Zeta36/chess-alpha-zero

for anyone interested

[schack]

It's a completely different project that uses some of the techniques from Alpha Zero. Not the same thing.

[corres]

[quote="kranium"]

There's a version of AlphaZero available here:
https://github.com/Zeta36/chess-alpha-zero
for anyone interested

[/quote]

Please read in the "readme.md" paragraph about "New Supervised Learning Pipeline".
You can read that human games was used before starting the learning process in the case of Zeta36 and AlphaZero too.
"...maybe chess is too complicated for a self training alone.."

[kranium]

It's a completely different project that uses some of the techniques from Alpha Zero. Not the same thing.

yes that's why I said 'a version'

[Steve Maughan]

I remember the experiments at the time. Could you briefly explain what you did? From memory I recall you did the following:

At the end of the game you parsed the list of moves and adjusted the score up or down a certain number of centipawns based on the outcome. You then hashed each position and stored it in a learning file. I assume this is then loaded into the hash table at the start of each game. Is this broadly correct?

Thanks,

Steve

[Milos]

No human games were loaded. Learning was accomplished thru millions of self-play games
The monte carlo search algorithm simply chose the move in each position with the highest win probability.

How do you explain these paragraphs from the paper:

"Training proceeded for 700,000 steps (mini-batches of size 4,096) starting from randomly initialised parameters"

"We represent the policy π(a|s) by a 8 × 8 × 73 stack of planes encoding a probability distribution over 4,672 possible moves. Each of the 8×8 positions identifies the square from which to “pick up” a piece."

"The number of games, positions, and thinking time varied per game due largely to different board sizes and game lengths, and are shown in Table S3."

So when playing self-played games positions used for training are taken from the games randomly (since position is part of set of training parameters). So what about starting positions of those 44 million training games? You think they were all random, or initial starting position and they had no chess knowledge in them????
Give me a break, thinking those ppl in Google are so stupid to train their network in such a lousy way, instead of sorting those 100'000 openings from the same chessbase they quote in the paper by probability of occurrence and using those statistics as starting positions for those self-played games.
Ofc in Table 2 they nicely show just percentages not actual numbers so you can't judge how many training games in total were from the starting position, because someone could be smart and sum up all those games from Table 2 and figure the number doesn't match 44 million...

Btw. 700'000 training iterations times 800 MTCS is already 56 million, not 44, so where did 12 million games disappear?

[kranium]

No human games were loaded. Learning was accomplished thru millions of self-play games
The monte carlo search algorithm simply chose the move in each position with the highest win probability.

How do you explain these paragraphs from the paper:

"Training proceeded for 700,000 steps (mini-batches of size 4,096) starting from randomly initialised parameters"

"We represent the policy π(a|s) by a 8 × 8 × 73 stack of planes encoding a probability distribution over 4,672 possible moves. Each of the 8×8 positions identifies the square from which to “pick up” a piece."

"The number of games, positions, and thinking time varied per game due largely to different board sizes and game lengths, and are shown in Table S3."

So when playing self-played games positions used for training are taken from the games randomly (since position is part of set of training parameters). So what about starting positions of those 44 million training games? You think they were all random, or initial starting position and they had no chess knowledge in them????
Give me a break, thinking those ppl in Google are so stupid to train their network in such a lousy way, instead of sorting those 100'000 openings from the same chessbase they quote in the paper by probability of occurrence and using those statistics as starting positions for those self-played games.
Ofc in Table 2 they nicely show just percentages not actual numbers so you can't judge how many training games in total were from the starting position, because someone could be smart and sum up all those games from Table 2 and figure the number doesn't match 44 million...

Btw. 700'000 training iterations times 800 MTCS is already 56 million, not 44, so where did 12 million games disappear?

My understanding is that "randomly initialised parameters" is not the same as loading human games.

Yes I assume (because it has not been made clear by Google) that the self-play games all started from the traditional start position.
AlphaZero would quickly realize that it was winning more often after 1. d4 than after 1. f3 for ex.

[Milos]

Yes I assume (because it has not been made clear by Google) that the self-play games all started from the traditional start position.
AlphaZero would quickly realize that it was winning more often after 1. d4 than after 1. f3 for ex.

They quote 100'000 games from chessbase and their batches are per 100'000 iterations of 800 MTCS simulations, another "coincidence"?

Again let me quote myself (btw. what do you think how many f3 openings are between those 100'000 openings from chessbase):

thinking those ppl in Google are so stupid to train their network in such a lousy way, instead of sorting those 100'000 openings from the same chessbase they quote in the paper by probability of occurrence and using those statistics as starting positions for those self-played games.