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Board-and-card games are those which involve higher level of uncertainty as it includes the probability of getting the right card and the moves made by other players. We look to model such games as Markov Games and find an optimal policy through the Minimax – Q algorithm. This will also be a test for the Minimax – Q algorithm to check how it performs in a situation with multiple goal states.

License: MIT License

Python 100.00%
card-game minimax algorithm uncertainty learning-algorithm markov-games board qlearning multiagent-reinforcement-learning multiagent-planning

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