4 citations · 5 across the 6 of their papers we have counts for
6 papers
Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
David Milec, Spyridon Samothrakis, Michael Fairbank +1
The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which naturally suits the online, non-stationary nature of self-play trainin…
Adapting Beyond the Depth Limit: Counter Strategies in Large Imperfect Information Games
David Milec, Vojtěch Kovařík, Viliam Lisý
We study the problem of adapting to a known sub-rational opponent during online play while remaining robust to rational opponents. We focus on large imperfect-information (zero-sum…
Generation of Games for Opponent Model Differentiation
David Milec, Viliam Lisý, Christopher Kiekintveld
Protecting against adversarial attacks is a common multiagent problem. Attackers in the real world are predominantly human actors, and the protection methods often incorporate oppo…
Continual Depth-limited Responses for Computing Counter-strategies in Sequential Games
David Milec, Ondřej Kubíček, Viliam Lisý
In zero-sum games, the optimal strategy is well-defined by the Nash equilibrium. However, it is overly conservative when playing against suboptimal opponents and it can not exploit…
Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms
Vojtěch Kovařík, David Milec, Michal Šustr +2
Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker. These a…
Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games
David Milec, Jakub Černý, Viliam Lisý +1
Solution concepts of traditional game theory assume entirely rational players; therefore, their ability to exploit subrational opponents is limited. One type of subrationality that…