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Professor Bryce
США
Добавлен 10 янв 2020
Davidson College
Data Structures for Deviation Payoffs (AAMAS Talk)
Paper abstract:
We present new data structures for representing symmetric normal-form games. These data structures are optimized for efficiently computing the expected utility of each unilateral pure-strategy deviation from a symmetric mixed-strategy profile. The cumulative effect of numerous incremental innovations is a dramatic speedup in the computation of symmetric mixed-strategy Nash equilibria, making it practical to represent and solve games with dozens to hundreds of players. These data structures naturally extend to role-symmetric and action-graph games with similar benefits.
Paper link: arxiv.org/abs/2302.13232
Julia library with the paper's experiments: github.com/Davidson-Game-Th...
We present new data structures for representing symmetric normal-form games. These data structures are optimized for efficiently computing the expected utility of each unilateral pure-strategy deviation from a symmetric mixed-strategy profile. The cumulative effect of numerous incremental innovations is a dramatic speedup in the computation of symmetric mixed-strategy Nash equilibria, making it practical to represent and solve games with dozens to hundreds of players. These data structures naturally extend to role-symmetric and action-graph games with similar benefits.
Paper link: arxiv.org/abs/2302.13232
Julia library with the paper's experiments: github.com/Davidson-Game-Th...
Просмотров: 614
Видео
Counterfactual Regret Minimization (AGT 26)
Просмотров 10 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 14 - Wednesday.
Sequential (and Perfect Bayesian) Equilibrium (AGT 25)
Просмотров 4 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 14 - Monday.
Subgame Perfection and Backwards Induction (AGT 24)
Просмотров 1,2 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 13 - Wednesday.
Extensive Form Games (AGT 23)
Просмотров 978Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 13 - Monday.
Action-Graph Games (AGT 22)
Просмотров 357Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 12 - Wednesday.
Congestion Games (AGT 21)
Просмотров 1 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 12 - Monday.
Data Structures for Symmetric Games (AGT 20)
Просмотров 331Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 11 - Wednesday. This video covers ideas from my recent paper: arxiv.org/abs/2302.13232
Gradient Descent for Nash (AGT 19)
Просмотров 445Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 11 - Monday.
Replicator Dynamics (AGT 18)
Просмотров 1,6 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 10 - Wednesday.
Fictitious Play and Regret Matching (AGT 17)
Просмотров 2,2 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 10 - Monday.
Complexity of Nash: PPAD (AGT 16)
Просмотров 579Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 9 - Wednesday.
Reductions and Why Zero Sum only Helps with Two Players (AGT 15)
Просмотров 336Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 9 - Monday.
Finding (Coarse) Correlated Equilibria with Linear Programming (AGT 14)
Просмотров 2 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 7 - Wednesday.
Finding Zero Sum Nash Equilibria with Linear Programming (AGT 13)
Просмотров 1,1 тыс.Год назад
Davidson CSC 383: Algorithmic Game Theory, S23. Week 7 - Monday.
Nash Algorithm Starting Points (AGT 12)
Просмотров 438Год назад
Nash Algorithm Starting Points (AGT 12)
Symmetric Games and Sperners Lemma (AGT 11)
Просмотров 628Год назад
Symmetric Games and Sperners Lemma (AGT 11)
Nash's Theorem: Every Game has an Equilibrium (AGT 10)
Просмотров 1,9 тыс.Год назад
Nash's Theorem: Every Game has an Equilibrium (AGT 10)
Equilibria with Pre-Commitment: Stackelberg & Coarse Correlated (AGT 09)
Просмотров 661Год назад
Equilibria with Pre-Commitment: Stackelberg & Coarse Correlated (AGT 09)
Nash Refinements: Trembling Hand and Evolutionary Stability (AGT 08)
Просмотров 1,9 тыс.Год назад
Nash Refinements: Trembling Hand and Evolutionary Stability (AGT 08)
Nash Approximation: ε-Equilibria (AGT 07)
Просмотров 759Год назад
Nash Approximation: ε-Equilibria (AGT 07)
Predicting Joint Behavior with Correlated Equilibria (AGT 06)
Просмотров 1,9 тыс.Год назад
Predicting Joint Behavior with Correlated Equilibria (AGT 06)
Predicting Strategies with Mixed Nash Equilibria (AGT 05)
Просмотров 821Год назад
Predicting Strategies with Mixed Nash Equilibria (AGT 05)
Predicting Actions with Dominance and Pure-Nash (AGT 04)
Просмотров 7542 года назад
Predicting Actions with Dominance and Pure-Nash (AGT 04)
Von Neumann-Morgenstern Utility (AGT 02)
Просмотров 2,9 тыс.2 года назад
Von Neumann-Morgenstern Utility (AGT 02)
Approximation Algorithms (Algorithms 25)
Просмотров 4,9 тыс.2 года назад
Approximation Algorithms (Algorithms 25)
Thank you so much for putting this out there, this is incredibly well-explained!
Amazing explanation
why do we use the derivative?
very well done and informative.
thank you so much, extremely useful, very clear!!
Thank you, this is the best example of a Stack Diagram I've seen.
Remarkable work, very good video format.
why are these videos so underrated???
best explanation
Best explanation I've seen for skip connections. Thanks!
You just explained it perfect! Thank you!
that was UNBELIEVABLY clear and understandable. THANK YOU
Holy shi dude I never really comment but this video is freakin good your explanations are on point and u broke down the architecture superbly
i love you mr. bryce!
Thank you professor. The best explanation, he includes influence of random weights on forward propagation.
Anna university
holy moly, this was a really good explanation!
Very very very clever reduction
professor Bryce , youre the GOAT!
Thank you for such a clear explanation
Thank you for a such clear video!!! Life saving!
thanks a lot sir! really well explained!
sir you deserves the most slopiest bj from my side gawk gawk thank you very much
Thank you for the clear and concise explanation.
The best explanation ever. Thank you professor
Great video
So perfect
Thank you for this great video. Why don't you use Kakutani's FPT? So we don't have to worry about the adv fnc being a one-to-one mapping?
geniusssssssssssssssss , my heart is so full after watching this video
I can't remember the last time I commented on a youtube video as it was much too long ago, but I just had to because your videos on deeplearning are CRIMINALLY underrated. I have yet to find another resource that explains ResNet so intuitively as you break down each concepts to laymen terms and you take your time explaining them. You have an amazing way of explaining concepts and I sincerely hope your videos get all the recognition they deserve!
This is so true!
Good day, am having difficulties understanding how to point out the independent set and vertex cover from a graph
@zachrayman7879 is correct that the cell (a,cf) should be (2,2). Reply is also correct in that in the subgame after player 1 plays b player 2 moves e so (a,cf) is not subgame perfect
I have been stuck at 6:56, why are the slack finish times: 1, 11, 15 and 17? Could you please explain the calculations behind that
Great explanation, it helped me a lot. Thank you for taking the time to make this video!
Wow amazing tutorial. Thanks ❤
Thank you very much, This is great. I'm watching this again and still super helpful. So delta is actually the upstream gradient, right? I only think that it would be nice to mention what is a computation graph, but otherwise, super helpful.
Thanks, you are the best ❤
great tutorial!
Bro is underrated asf
very nice 🎉
thank you so so much for this video!
Wow, so clear! That was stellar, thank you!
A true legend
shouldn't folding have a utility or expected value of zero. since no money goes into the pot how can it return -1 in value?
thanks. Sorry but the video you mentioned in the last seconds of this video in not yet uploaded(video about stability proof)
That was amazing! So clear and concise explanation. Thanks!
Thank you for your good explanation, helped me a lot on my deep understanding journey of all these mechanims 😊
AMAZING!!
In this example iI think Sj >= Fgi because if Sj is smaller then there is a conflict in task and the task is not compatible instead the Si should be after the finish time of last request