Here are my slides for part 2.

Suggested starting points for anybody new to CFR/Poker (see reference below):

- Model AI Assignments, Intro to CFR Tutorial: Neller and Lanctot '13 (intended for undergrads)
- Sandholm '10 AI Magazine article
- Rubin & Watson '11 survey
- Michael Johanson's PhD Thesis

- Abou Risk, N. & Szafron, D. (2010). Using counterfactual regret minimization to create competitive multiplayer
poker agents. In
*AAMAS*, pages 159-166. - Bosansky, B., Kiekintveld, C., Lisy, V., and Pechoucek, M. (2014). An Exact Double-Oracle Algorithm for
Zero-Sum Extensive-Form Games with Imperfect Information. In
*Journal of Artificial Intelligence Research*51, pp. 829-866. - Bowling, M., Burch, N., Johanson M., and
Tammelin, O.. (2015). Heads-up limit hold'em poker is solved.
*Science*, 347(6218):145-149. (Solving HULHE paper.) - Brown, N. and Sandholm, T. (2014). Regret Transfer and Parameter Optimization. In
*Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)*. - Brown, N. and Sandholm, T. (2015). Simultaneous Abstraction and Equilibrium Finding in Games. In
*Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)*. - Brown, N. and Sandholm, T. (2015). Regret-Based Pruning in Extensive-Form Games. In
*Neural Information Processing Systems (NIPS)*. - Brown, N. and Sandholm, T. (2016). Strategy-Based Warm Starting for Regret Minimization in Games. In
*Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)*. - Burch, N., Johanson, M. , Bowling, M.. Solving
imperfect information games using decomposition. (2014). In
*28th AAAI Conference on Artificial Intelligence*. (CFR-D.) - Chen, K. and Bowling, M. (2012). Tractable Objectives for Robust Policy Optimization.
In
*Advances in Neural Information Processing Systems 25*(NIPS), pp. 2078-2086. - Gatti, N., Panozzo, F., and Restelli, M. (2013). Efficient
evolutionary dynamics with extensive-form games. In
*27th AAAI Conference on Arti cial Intelligence (AAAI)*. - Gilpin, A., Hoda, S., Peña, J., and Sandholm, T.. Gradient-based algorithms for finding Nash equilibria
in extensive form games. In
*Workshop on Internet and Network Economics (WINE-07)*, 2007. (EGT conference paper.) - Gibson, R., and Szafron, D. (2011). On Strategy Stitching in Large Extensive Form Multiplayer Games.
In
*Advances in Neural Information Processing Systems 24*. - Gibson, R., Lanctot, M., Burch, N., Szafron, D., and Bowling, M. (2012).
Generalized sampling and variance in counterfactual regret minimization.
In
*Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (AAAI-12)*. - Gibson, R. Burch, N., Lanctot, M. and Szafron, D. (2012). Efficient Monte
Carlo counterfactual regret minimization in games with many player actions. In
*Advances in Neural Information Processing Systems 25*. - Gibson, R. (2014). Regret Minimization in Non-Zero-Sum Games with Applications to Building Champion Multiplayer Computer Poker Agents. arxiv.org/abs/1305.0034 (Multiplayer and dominated actions paper.)
- Gibson, R. (2014). Regret Minimization in Games and the Development of Champion Multiplayer Computer Poker-Playing Agents. PhD Thesis, University of Alberta.
- Hart, S., & Mas-Colell, A. (2000). A simple adaptive procedure leading to correlated equilibrium.
*Econometrica*, 68(5), 1127-1150. (Regret matching.) - Heinrich, J. and Silver, D. (2015). Smooth UCT search in computer poker. In
*IJCAI*. - Heinrich, J., Lanctot, M., and Silver, D. (2015). Fictitious Self-Play in Extensive-Form Games.
In
*Proceedings of International Conference on Machine Learning*. - Heinrich, J. and Silver, D. (2016). Deep Reinforcement Learning from Self-Play in Imperfect-Information Games. arxiv.org/abs/1603.01121
- Hoda, S., Gilpin, A., Pena, J., & Sandholm, T. (2010). Smoothing techniques for computing Nash equilibria of sequential games. Mathematics of Operations Research, 35(2), 494-512. (EGT journal paper.)
- Jackson, E. (2012). Slumbot: An implementation of counterfactual regret minimization on
commodity hardware. In
*Proceedings of AAAI 2012 Poker Symposium*, 2012. - Jackson, E. (2014). A Time and Space Efficient Algorithm for Approximately
Solving Large Imperfect Information Games. In
*Proceedings of AAAI 2014 Computer Poker and Imperfect Information Workshop*. -
Johanson, M., Bard, N., Burch, N., and Bowling, M. (2012).
Finding Optimal Abstract Strategies in Extensive-Form Games.
In
*Proceedings of Twenty-Sixth AAAI Conference.*(CFR-BR paper.) - Johanson, M., Bard, N., Lanctot, M., Gibson, R., and
Bowling, M.. (2012). Efficient Nash equilibrium approximation
through Monte Carlo counterfactual regret
minimization. In
*Proceedings of the Eleventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)*. - Johanson, M., & Bowling, M. (2009). Data biased robust counter strategies. In
*Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS)*, pp. 264-271. - Johanson, M., Zinkevich, M., & Bowling, M. (2008). Computing robust counter-strategies. In
*Advances in Neural Information Processing Systems 20 NIPS*. (Restricted Nash responses.) - Johanson, M.B. (2016). Robust Strategies and Counter-Strategies: From Superhuman to Optimal Play. PhD Thesis, Department of Computing Science, University of Alberta.
- Kroer, C., and Sandholm, T. (2014.) Extensive-form game imperfect-recall abstractions with bounds. arXiv preprint arXiv:1409.3302.
- Kroer, C., & Sandholm, S.. Imperfect-Recall Abstractions with Bounds in Games. (2016).
*ACM conference on Economics and Computation (EC)*. - Lanctot, M., Waugh, K., Zinkevich, M., & Bowling, M. (2009). Monte Carlo sampling for regret
minimization in extensive games. In
*Advances in Neural Information Processing Systems 22 (NIPS)*, pp. 1078-1086. (MCCFR paper.) - Lanctot, M., Gibson, R., Burch, N. and Bowling, M. (2012). No-regret learning in extensiveform
games with imperfect recall. In
*Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML 2012)*. - Lanctot, M. (2014).
Further Developments of Extensive-Form Replicator Dynamics using the Sequence-Form Representation.
In
*AAMAS*. - Lisy, V., Lanctot, M. and Bowling, M. (2015). Online monte carlo counterfactual regret minimization
for search in imperfect information games. In
*Proceedings of the Fourteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)*. - Lisy, V., Davis, T., and Bowling. M. (2016). Counterfactual Regret Minimization in Sequential
Security Games. In
*AAAI*. - Neller, T.W. and Hnath, S. (2011). Approximating optimal Dudo play with fixed-strategy
iteration counterfactual regret minimization. In
*Computers and Games*, 2011. - Neller, T.W. and Lanctot, M. (2013). An Introduction to Counterfactual Regret Minimization.
In
*Proceedings of Model {AI} Assignments, The Fourth Symposium on Educational Advances in Artificial Intelligence ({EAAI}-2013)*. link to site. - Osborne, M.J. and Rubinstein, A. (1994). A course in game theory. MIT Press. ISBN 0-262-65040-1.
- Ponsen, M., de Jong, S., and Lanctot, M. (2011). Computing approximate Nash equilibria and
robust best-responses using sampling.
*Journal of Artificial Intelligence Research*, 42:575-605. - Rubin, J. and Watson, I. (2011). Computer poker: A review.
*Artificial Intelligence*, 175(5-6):958-987. - Sandholm, T. (2010). The state of solving large incomplete information games, and application
to poker.
*AI Magazine*13-32. Special issue on Algorithmic Game Theory. - von Stengel, B. (2007). Equilibrium computation for two-player games in strategic
and extensive form. In
*Algorithmic Game Theory, chapter 4*. Cambridge University Press. - Szafron, D., Gibson, R., Sturtevant, N. (2013).
A Parameterized Family of Equilibrium Profiles for Three-Player Kuhn Poker.
In
*Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS).* - Tammelin, O., Burch, N., Johanson, M. and Bowling, M. (2015). Solving Heads-up Limit Texas Hold'em.
In
*Proceedings of IJCAI*. (CFR+ paper). - Waugh, K., Zinkevich, M., Johanson, M., Kan, M., Schnizlein, D., & Bowling, M. (2009). A practical
use of imperfect recall. In
*Proceedings of the 8th Symposium on Abstraction, Reformulation and Approximation (SARA)*. - Waugh, K., Morrill, D., Bagnell, J.A., and Bowling, M. (2015). Solving Games with Functional Regret Estimation.
Kevin Waugh, Dustin Morrill, J. Andrew Bagnell, and Michael Bowling.
In
*AAAI Conference on Artificial Intelligence*. - Waugh, K. and Bagnell, J.A. (2015). A Unified View of Large-scale Zero-sum Equilibrium Computation.
Kevin Waugh and J. Andrew Bagnell. In
*AAAI Workshop on Computer Poker and Imperfect Information*. - Zinkevich, M., Bowling, M., and Burch, N. (2007). A New Algorithm for Generating Equilibria in Massive Zero-Sum Games.
In
*Proceedings of National Conference on Artificial Intelligence (AAAI)*, pp. 788-793. - Zinkevich, M., Johanson, M., Bowling, M., & Piccione, C. (2007). Regret minimization in games
with incomplete information. In
*Advances in Neural Information Processing Systems 20 NIPS*. (CFR paper.)