4 ms·

I couldn't find it confirmed in the primary or secondary article, but I would bet the bot is just playing cash at a fixed stack depth rather than a tournament;

by MFLoon 7y ago

I couldn't find it confirmed in the primary or secondary article, but I would bet the bot is just playing cash at a fixed stack depth rather than a tournament; just like in the wild, bots are much more of a problem in online cash than online tournaments. Dynamically adjusting strategies by stack depth, number of players, and pay jumps, would probably be several orders of magnitude more complex.

dmoy 7y ago
> There were two formats for the experiment: five humans playing with one AI at the table, and one human playing with five copies of the AI at the table. In each case, there were six players at the table with 10,000 chips at the start of each hand. The small blind was 50 chips, and the big blind was 100 chips. In the fb article linked above.
- MFLoon 7y ago
  Ah thanks. As I suspected, cash game with fixed 100bb stacks.
bcassedy 7y ago
Smaller stack sizes reduce possibilities and thus reduce complexity. Pay jumps result in chips having different utility to each player which forces some situational playstyles to be more optimal. I would guess that this also reduces the complexity of the game. Since tournaments don't often spend much time with stacks much deeper than 100bb, I would guess that tournaments would be more easily solved. Though tournaments are much more frequently run with 9-10 players rather than 6 at a table. https://www.cardplayer.com/poker-news/18226-explain-poker-like-i-m-five-independent-chip-model-icm https://www.cardplayer.com/poker-news/18226-explain-poker-li...
- MFLoon 7y ago
  You're right that a single short stack hand in a vacuum has fewer game tree branches, and that factoring in chip utility is also fairly straightforward. But I strongly disagree that it reduces the overall complexity of the game. The model in the article played every single hand with 100bb; to be an effective tournament player it would have to be able to fluidly adjust strategies between big, medium and short stack play, as well as reasoning about the stack sizes of other players at the table. It's basically 4 different games at >100bb, 50-100bb, 25-50bb, and <25bb, so it would have to develop optimal strategies for each. And even if the shallower stacked games are generally simpler in isolation, there's a meta strategy of knowing which one to apply in a given hand with heterogenous stack sizes. To paraphrase Doug Polk "If cash game play is a science, tournaments are more of an art."
  - maehwasu 7y ago
    The bot could likely just be trained on the 4 or so different games. You’re likely increasing the complexity by a constant factor, nothing exponential here.

by MFLoon 7y ago

dmoy 7y ago

> There were two formats for the experiment: five humans playing with one AI at the table, and one human playing with five copies of the AI at the table. In each case, there were six players at the table with 10,000 chips at the start of each hand. The small blind was 50 chips, and the big blind was 100 chips. In the fb article linked above.

MFLoon 7y ago
Ah thanks. As I suspected, cash game with fixed 100bb stacks.

bcassedy 7y ago

Smaller stack sizes reduce possibilities and thus reduce complexity. Pay jumps result in chips having different utility to each player which forces some situational playstyles to be more optimal. I would guess that this also reduces the complexity of the game. Since tournaments don't often spend much time with stacks much deeper than 100bb, I would guess that tournaments would be more easily solved. Though tournaments are much more frequently run with 9-10 players rather than 6 at a table. https://www.cardplayer.com/poker-news/18226-explain-poker-like-i-m-five-independent-chip-model-icm https://www.cardplayer.com/poker-news/18226-explain-poker-li...

MFLoon 7y ago
You're right that a single short stack hand in a vacuum has fewer game tree branches, and that factoring in chip utility is also fairly straightforward. But I strongly disagree that it reduces the overall complexity of the game. The model in the article played every single hand with 100bb; to be an effective tournament player it would have to be able to fluidly adjust strategies between big, medium and short stack play, as well as reasoning about the stack sizes of other players at the table. It's basically 4 different games at >100bb, 50-100bb, 25-50bb, and <25bb, so it would have to develop optimal strategies for each. And even if the shallower stacked games are generally simpler in isolation, there's a meta strategy of knowing which one to apply in a given hand with heterogenous stack sizes. To paraphrase Doug Polk "If cash game play is a science, tournaments are more of an art."
- maehwasu 7y ago
  The bot could likely just be trained on the 4 or so different games. You’re likely increasing the complexity by a constant factor, nothing exponential here.