RL_flappy_bird

Overview

This project implements Reinforcement Learning (RL) algorithms to train a model to play Flappy Bird game. The code is designed to provide a demonstration of Q-learning in action, showcasing how an agent learns to navigate and improve its performance in the Flappy Bird environment.

Table of Contents

• Introduction
• Installation
• Usage
• Algorithm Details
• Customization

Introduction

The SmartFlappyBird class is the main component of this project, incorporating the Q-learning algorithm to train an agent to play Flappy Bird. The game environment is provided by the flappy_bird_gym library, and the core RL functions are implemented in the main file.

Installation

To run the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/mobinbr/RL_flappy_bird

2. Navigate to the project directory:

   cd RL_flappy_bird

I suggest creating a virtual environment before proceeding with the following steps to mitigate potential issues.

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Run the SmartFlappyBird class by executing the main file:

python main.py

This will initiate the training process for the specified number of iterations; wait for a few seconds (30 seconds or more for training)
you can speed up the program by lowering the FPS.
After training, the model will run in the Flappy Bird environment to demonstrate its learned behavior.

Algorithm Details

The Q-learning algorithm is implemented in the SmartFlappyBird class. Key components include:

• Q-values: The Q-values are stored in a Counter, and the Q-learning formula is used to update these values during training.

• Policy Function: The policy method determines the action to be taken based on the current state, either by exploiting the learned Q-values or by choosing a random action with a certain probability.

• Reward Function: The compute_reward method calculates the reward for a given action, encouraging the agent to learn optimal strategies for maximizing its score.

• Training Loop: The run_with_policy method is responsible for training the model using the Q-learning algorithm. The run_with_no_policy method demonstrates the model's performance after training.

Customization

Adjust the parameters in the SmartFlappyBird class and the bird speed to customize the training process. Experiment with hyperparameters such as epsilon, alpha, and lambda to observe their impact on the agent's learning.