COMP90054 (AI planning for Autonomy) In-class competition: Creating AI agents for Azul game.
This project was done as the final project of a subject (COMP90054 - AI planning for autonomy).
Ranked 12th out of 68 teams.
Our team implemented 3 algorithms: 1) Monte Carlo Tree Search, 2) Deep Q-learning, 3) Minimax.
Among above algorithms, I implemented both DQN and Minimax.
For our final agent, Minimax player was chosen.
Naive player (player0-top) vs DQN player (player1-bottom) : DQN won! (23-24)
In the demo, player0 (top) is naive player (baseline - simple strategy) and player1 (bottom) is DQN player.
At the end of the game, DQN player beated naive player only by 1 point (23-24), which is not powerful... It was possibly because the state space for action is too large. Maybe it could end up with the better result if forcing a bad reward for unnecessary (totally meaningless) actions for some initial replay periods like in "unnecessary action filtering" in Minimax (see Design of Agent).
Implementation of model training for DQN player is in dqn_train_model.py and implementation of DQN player is in /Alpha_Azul_20/dqn_player.py
Naive player (player0-top) vs Minimax player (player1-bottom) : Minimax won! (16-32)
In the demo, player0 (top) is naive player (baseline - simple strategy) and player1 (bottom) is Minimax player.
At the end of the game, our Minimax player beated naive player by double (16-32), which is very powerful.
Look at the Design of the Agent. Since the state space for action is too large, "Action filtering" as well as alpha-beta pruning was used to filter unnecessary (totally meaningless) actions while making the agent being able to explore deeper with smaller action state space.
In addition, many other strateges were considered in evaluation metrics to maximize the gap in points earned between enemy and my player (see my code below).
Implementation of Minimax player is in /Alpha_Azul_20/minimax_player.py
