An implementation of the MCTS algorithm as an adversarial game playing agent.

Work is being done along with John Bourassa.

• Nested Tic-Tac-Toe

Tic-Tac-Toe, but each square on the outer board is another Tic-Tac-Toe board.

The game is played as standard Tic-Tac-Toe, except there are 9 inner boards to make moves on. Winning an inner board claims that space for the winner on the outer board. Draws result in a square that does not count for either side. Winning the game requires winning 3 inner boards in a row, forming a win on the outer board.

• Havannah

The game of Havannah is played on a hex board that is typically 10 hexes to a side.

Players alternate turns placing stones, or coloring hexes in our case, in previously unchosen hexes. Play continues until one player has formed one of three different winning configurations: a ring, fork, or bridge.

• MCTSAgent

Planning agent that makes decisions based on Monte Carlo Tree Search.

Computes as many runs as possible in the time alloted, where a run consists of the following stages:

Selection - Start at root, use UCB to choose actions until action does not produce child
Expansion - Create new node
Simulation - Play out game until reaching a terminal state
Backpropagation - Update win/trial counters for new node and all parents

Then, it chooses the action with the most trials and returns that.

• RandomAgent

Agent that chooses random actions regardless of the game state.

• HumanAgent

Agent that relies on user input to make action choices.

Tested on Python version 3.6, but should work on any recent version of Python 3.

Run python main.py -h to see the available options for games and agents.

An example game of Nested Tic-Tac-Toe can be shown by running python main.py ttt, which will start the game with a MCTSAgent against a RandomAgent with a default time-allowed of 0.5 seconds.