Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input. However calculating the precise probability using combinatorics is an intractable problem, so instead we approximate it. Monte Carlo simulation is an effective technique that can be used to approximate the probability that a player will win and or tie a hand. However, without the use of a memory-intensive lookup table or a supercomputer, it becomes infeasible to run millions of times when training an agent with self-play. To combat the space-time tradeoff, we use deep learning to approximate the probabilities obtained from the Monte Carlo simulation with high accuracy. The learned model proves to be a lightweight alternative to Monte Carlo simulation, which ultimately allows us to use the probabilities as inputs during self-play efficiently. The source code and optimized neural network can be found at this https URL
Another paper used a Support Vector Machine (SVM) classifier to determine whether or not you will win the hand. They then used the confidence score from the classifier as an estimate of the probability of winning @cite_3 . Even though they showed that their classifier achieved precision of $>60
@cite_3
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Among all the technologies in creating a good poker agent, estimating winning probability is a key issue. In this paper, we propose an approach to estimating winning probability for Texas Hold'em poker. We design a data structure using both the observable data from the current board and the history. A Support Vector Machine classifier is trained and 5-fold cross-validation is employed. We create a poker agent with some decision making strategies to compete. Experimental results show that our method has outperformed three other agents in precision of estimating winning probability.