Autoplay
What does autoplay do?
Autoplay pits two Macondo bots against each other for many games. In
its basic use case, without any arguments, autoplay will use two
“exhaustive leave” players - i.e. computer players that use 1-to-6 tile
leave values to calculate equity.
The NWL23 leave values can be found in ./data/strategy/NWL23/leaves.klv2. Slightly different values for CSW24 are found in ./data/strategy/CSW24/leaves.klv2.
See How to make leaves and GADDAG files for how this file was created.
Options
-logfile foo.txt will log games to the given file. Note that another file will be created, games-foo.txt in this case, with very basic metadata
about each game (the final score and who went first). foo.txt will contain more-in depth turn-per-turn data.
-lexicon CSW19 uses the CSW19 lexicon, for example
-letterdistribution norwegian uses the norwegian letter distribution, for example
-leavefile1 filename.klv2 sets the first bot’s leavefile to filename.klv2. Note that the filename.klv2 must be located inside the ./data/strategy/<lexicon> directory in order to be found.
-leavefile2 filename.klv2 sets the second bot’s leavefile to filename.klv2.
Starting and stopping
Using any autoplay command will start the games. Every 1000 games, a log will be printed to screen.
To STOP autoplay cleanly, type in autoplay stop. It will take a couple of seconds.
Analyzing log files
A very simple analyzer can be accessed with the autoanalyze command. This command’s only argument is the games-foo.txt file that was created with the -logfile option.
Examples
Player1 is an exhaustive-leave bot, Player2 is a greedy bot:
autoplay -botcode2 NO_LEAVE_BOT
Leave it running for a bit, then do an autoplay stop and then an autoanalyze /tmp/games-autoplay.txt:
Games played: 20111
exhaustiveleave-1 wins: 12967.0 (64.477%)
exhaustiveleave-1 went first: 10169.0 (50.564%)
Player who went first wins: 10971.0 (54.552%)
exhaustiveleave-1 Mean Score: 434.577545 Stdev: 59.587155
noleave-2 Mean Score: 399.649893 Stdev: 58.249322
The stats above show that a bot that uses leave values wins nearly 2/3 of its games against a greedy bot.
Both players are exhaustive-leave bots:
autoplay (this is the default - HASTY_BOT, and uses default Macondo values)
Player1 uses a special set of leave values, Player2 uses the default set:
autoplay -leavefile1 quackleleaves.klv2
Note: The file quackleleaves.klv2 in this case must be in your ./data/strategy/<lexicon>/ directory.
Analysis:
Games played: 84648
exhaustiveleave-1 wins: 42923.0 (50.708%)
exhaustiveleave-1 went first: 42425.0 (50.119%)
Player who went first wins: 47021.0 (55.549%)
exhaustiveleave-1 Mean Score: 435.499787 Stdev: 58.757773
exhaustiveleave-2 Mean Score: 434.291584 Stdev: 59.496489
This analysis shows that the bot that used Quackle leaves won around 50.7% of its games against Macondo. A binomial calculator shows that the chance that this is a fluke is around 0.00002.
Note: Macondo values can now beat Quackle values around 50.6 or so % of the time, after fine-tuning our values. You can see the /notebooks directory of this repo for more details.
We don’t have a simming player working yet, but I think the difference might be even bigger once simulation is involved.
Different bot levels
autoplay -botcode1 LEVEL4_PROBABILISTIC -botcode2 HASTY_BOT -numgames 20000
This plays 20000 games between a Level 4 probabilistic bot and a HastyBot. “Probabilistic” bots miss more plays than HastyBot randomly; the weaker, the more plays it misses.
You can see macondo.proto in the source for a list of the different bots.
autoplay -lexicon FRA20 -letterdistribution french -botcode1 LEVEL1_PROBABILISTIC -botcode2 HASTY_BOT
In order to specify a non-English lexicon, you must also specify the letter distribution, as in the example above.