709 research outputs found
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Learning strategies for imperfect information games from samples of
interaction is a challenging problem. A common method for this setting, Monte
Carlo Counterfactual Regret Minimization (MCCFR), can have slow long-term
convergence rates due to high variance. In this paper, we introduce a variance
reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR.
Using this technique, per-iteration estimated values and updates are
reformulated as a function of sampled values and state-action baselines,
similar to their use in policy gradient reinforcement learning. The new
formulation allows estimates to be bootstrapped from other estimates within the
same episode, propagating the benefits of baselines along the sampled
trajectory; the estimates remain unbiased even when bootstrapping from other
estimates. Finally, we show that given a perfect baseline, the variance of the
value estimates can be reduced to zero. Experimental evaluation shows that
VR-MCCFR brings an order of magnitude speedup, while the empirical variance
decreases by three orders of magnitude. The decreased variance allows for the
first time CFR+ to be used with sampling, increasing the speedup to two orders
of magnitude
A Unified View of Large-scale Zero-sum Equilibrium Computation
The task of computing approximate Nash equilibria in large zero-sum
extensive-form games has received a tremendous amount of attention due mainly
to the Annual Computer Poker Competition. Immediately after its inception, two
competing and seemingly different approaches emerged---one an application of
no-regret online learning, the other a sophisticated gradient method applied to
a convex-concave saddle-point formulation. Since then, both approaches have
grown in relative isolation with advancements on one side not effecting the
other. In this paper, we rectify this by dissecting and, in a sense, unify the
two views.Comment: AAAI Workshop on Computer Poker and Imperfect Informatio
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass
In the last decades we have witnessed the success of applications of
Artificial Intelligence to playing games. In this work we address the
challenging field of games with hidden information and card games in
particular. Jass is a very popular card game in Switzerland and is closely
connected with Swiss culture. To the best of our knowledge, performances of
Artificial Intelligence agents in the game of Jass do not outperform top
players yet. Our contribution to the community is two-fold. First, we provide
an overview of the current state-of-the-art of Artificial Intelligence methods
for card games in general. Second, we discuss their application to the use-case
of the Swiss card game Jass. This paper aims to be an entry point for both
seasoned researchers and new practitioners who want to join in the Jass
challenge
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