MNK game environment for Deep RL.

On Github


This project includes the following:

  • A Board object which can have a variable number of rows and columns.
  • A Win detection function which can detect wins for variable number of required tokens in a row.
  • A Game Tree which represents the sequence of moves made and pruned explorations from those moves.
  • Many Agents, ranging from a trivial bot which simply makes randomized moves up to more sophisticated bots using Minimax, Tabular Q Learning, and Deep Q Learning.
  • A Competition function to train bots with self-play and also to compare different bots against each other.
  • Tests with extensive coverage for all of the above.