/Multi-Agent-Alternating-Turn-Board-and-Card-Games

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.

Primary LanguagePythonMIT LicenseMIT

Multi Agent Alternating Turn Board and Card Games

Authors

In Alphabetic Order

About the Project

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.

Python Packages Used

  • PyGame - To visualise the simulated gameplay
  • PyDealer - To implement a deck of cards used during game play

Prject Files:

  • Game.py - Main Run File. Simulate game between players.
  • Sequence.py - Defines the board and maintains the card deck.
  • Qlearn.py - Q Learning Algorithm Implementation.
  • minimaxQ.py - Minimax Q Learning Algorithm Implementation.