To setup the game, clone this repository and install the dependencies:
pip install -r requirements.txt
To start playing a game, we need to implement agents. For example, to play the game using two random agents (agents which take a random action), run the following:
python simulator.py --player_1 random_agent --player_2 random_agent
This will spawn a random game board of size NxN, and run the two agents of class RandomAgent. You will be able to see their moves in the console.
To visualize the moves within a game, use the --display
flag. You can set the delay (in seconds) using --display_delay
argument to better visualize the steps the agents take to win a game.
python simulator.py --player_1 random_agent --player_2 random_agent --display
To play the game on your own, use a human_agent
to play the game.
python simulator.py --player_1 human_agent --player_2 random_agent --display
On an M x M chess board, 2 players are randomly distributed on the board with one player occupying one block.
In each iteration, one player moves at most K
steps (between 0
and K
) in either horizontal or vertical direction, and must put a barrier around him or her in one of the 4 directions except the boarders of the chess board. The players move in a round-robin way.
- Each player cannot go into other player's place or put barriers in areas that already have barriers.
- Currently the maximal number of steps is set to
K = (M + 1) // 2
.
The game ends when each player is separated in a closed zone by the barriers and boundaries. The final score for each player will be the number of blocks in that zone.
Each player should maximize the final score of itself, i.e., the number of blocks in its zone in the endgame.