Consistent Opponent Modeling of Static Opponents in Imperfect-Information Games
This work addresses a key limitation in multi-agent AI for imperfect-information games, offering a more reliable method for exploiting static opponents, though it is incremental as it focuses on a specific scenario.
The paper tackles the problem of opponent modeling in imperfect-information games, showing that existing methods fail to guarantee convergence to an opponent's true strategy even with infinite data, and proposes a new algorithm that efficiently achieves this property using convex minimization and projected gradient descent.
The goal of agents in multi-agent environments is to maximize total reward against the opposing agents that are encountered. Following a game-theoretic solution concept, such as Nash equilibrium, may obtain a strong performance in some settings; however, such approaches fail to capitalize on historical and observed data from repeated interactions against our opponents. Opponent modeling algorithms integrate machine learning techniques to exploit suboptimal opponents utilizing available data; however, the effectiveness of such approaches in imperfect-information games to date is quite limited. We show that existing opponent modeling approaches fail to satisfy a simple desirable property even against static opponents drawn from a known prior distribution; namely, they do not guarantee that the model approaches the opponent's true strategy even in the limit as the number of game iterations approaches infinity. We develop a new algorithm that is able to achieve this property and runs efficiently by solving a convex minimization problem based on the sequence-form game representation using projected gradient descent. The algorithm is guaranteed to efficiently converge to the opponent's true strategy given observations from gameplay and possibly additional historical data if it is available.