spears wrote:
- What is the timescale for this work?
- Could you summarize the current project objectives and plan?
- I'm wondering if you could use some overall project advice, rather than technical details which is what I've concentrated on so far.
- Given your initial stated objective, the strength mean/variance is something of a distraction if you are in a hurry. You could use ehs2 see page 25 of
http://poker.cs.ualberta.ca/publication ... on.msc.pdf- In about two weeks, I've to hand in my research.
- Goal is to build an as good as possible pokerbot (NL-multiplayer) with adaptive opponent modeling. I hope to have a working pokerbot by the beginning of next week. Then I've got a week for tweaking, testing and improving.
But the goal is not the most important part. It's more important I can show research and development, so don't worry about the result too much.
- My supervisor wasn't present today, so a final decision on the bucketing will be for tomorrow, but I'm guessing we will raise the means by some power (and maybe also the variance instead of a dummy point) and then use a clustering algorithm. The end results will not differ much though.
And with that, the outline of the bucketing is finished. I'll calculate the mean and variance after every possible flop for every hole, so I can create transition tables. Then do the same for flop->turn and turn->river.
(You are very kind to help me so much, and I really appreciate it!)
So, next problem: the simplified gamestate I =
how can I describe the gamestate with a small number of features, so I can accurately model (most of) the opponents' possible strategies P(a | b*, I)?I'm thinking the beliefs distribution b* of the opponents holecards has already much of the information about previous actions of the opponent, so I don't need to include such information here (correct me if I'm wrong).
Here is a first thought about the features I'll use:
- round (I'm thinking about eventually using a different model for each round, or preflop-postflop, but for now, I'll use only one)
- relative stacksize player (vs. potsize)
- absolute stacksize player (in BB)
- position (only against players still in the hand)
- relative amount to call (vs stacksize)
- absolute amount to call (in BB)
- size last raise (in BB)
- number of opponents (at the start of the hand)
- number of active opponents (= players who are still in the hand)
- average stacksize active opponents (or should I use max(player stacksize, opponent stacksize) and take the average of that?)
- average VPIP, AF and frequency actions active opponents
- number of opponents that raised this round
- average stacksize of active opponents that raised this round (same note as above)
- average VPIP, AF and frequency actions of active opponents that raised this round
- number of players all-in
- number of hands played
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I've also thought about:
1. The players own statistics (VPIP, AF, frequency actions), but I'm not sure cause the opponent does not make decisions based on his own statistics. Furthermore, it might cause the model to concentrate too much on these features, I guess. It should be a great help for the default model, as this would lead to different strategies for different characterised players. But for an opponent-specific model, only statistics based on his last x actions/hands would make a difference, right?
2. Include information about the belief distribution of the opponents, as this implies information about the action sequence. (That would mean I'll have to keep a belief distribution for myself too).
3. If there are any statistics I don't need to use cause I'm using MCTS.
4. Information about the board (eg dry/wet board). I've statistics of this with the calculation of the bucketing. I don't think they are already implied in the beliefs distribution, so I use give for example the mean variance of the specific board for all possible holecards as a feature.
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For the model, I'm thinking to use a NN with the belief distribution and the described gamestate as input, but I'll consult my supervisor for that one too.