FlapAI-Bird

This AI program implements several AI agents for playing Flappy Bird. The program applies reinforcement learning techniques, including SARSA, Q-Learning, and Function Approximation. After training for 10,000 iterations, the agents regularly achieves high scores of 1400+, with the highest in-game score of 2069.

Installation

The project requires the following frameworks:

Train an Agent

The program implements the following agents.

  • Baseline Agent.
python main.py --algo Baseline --probFlap 0.5
  • SARSA Agent.
python main.py --algo SARSA --probFlap 0.1 --rounding 10 --lr 0.8
  • Q-Learning Agent.
python main.py --algo QLearning --probFlap 0.1 --rounding 10 --lr 0.8 --order backward
  • Function Approximation Agent with Linear Regression.
python main.py --algo FuncApproxLR --probFlap 0.1 --lr 0.1
  • Function Approximation Agent with a Feed Forward Neural Network.
python main.py --algo FuncApproxDNN --probFlap 0.1 --lr 0.1
  • Function Approximation Agent with Convolutional Neural Network.
python main.py --algo FuncApproxCNN --probFlap 0.1 --lr 0.1

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