README.md

Deep-Q-Network DQN Variants Analysis for Optimization

Programmatically recorded the best strategy's videos for each algorithm and each environment.

CartPole Demo Dueling Model

Lunar lander Demo Dueling Model

Mountain Car Demo Dueling Model

Analysis result:

CartPole Environment Checkpoint Comparison

Lunar Lander Environment Checkpoint Comparison

Mountain Car Environment Checkpoint Comparison

Overall Model summary

My Presentation PPT: Click here to see the full presentation

Project Overview

Each video saved inside "results/video" directory which contain one video for each environment and each algorithm only best strategy using gymnasium package.

In the weights directory have trained file 1000.pth, 2000.pth, best.pth and training.log for each algorithm and each environment.

plots_images directory contain all visualization 7 images and a checkpoint_summary.csv file for each environments and algorithms

Installation

Please follow these steps to install the necessary dependencies and set up the project locally. To see required packages please open the requirements.txt file.

1. Clone the repository:

git clone https://github.com/raselmahmud-coder/RL_Experiment_2.git
cd RL_Experiment_2
pip install -r requirements.txt

For Training the Project:

In this project have 3 algorithms "DQN", "DoubleDQN", "DuelingDQN" and 3 environments "CartPole-v1", "MountainCar-v0", "LunarLander-v3"

You need to change the algorithm and environment argument for sequential training.

python main.py --algorithm DQN --environment CartPole-v1     

For Visualization the Project:

We have 3 environment here:

• "CartPole-v1"
• "MountainCar-v0"
• "LunarLander-v3"

For visual and compare you need to set i.e., env_name = 'LunarLander-v3' manually need to change for each environment name and then run below command it will save specific directory each algorithms plot.

python .\results\visualize_comparison.py    
python .\results\compare_checkpoints.py    

File Hierarchy

📦RL_Experiment_2
 ┣ 📂results
 ┃ ┣ 📂plots_images
 ┃ ┃ ┣ 📂CartPole-v1_ENV
 ┃ ┃ ┃ ┣ 📜checkpoint_comparison.png
 ┃ ┃ ┃ ┣ 📜checkpoint_summary.csv
 ┃ ┃ ┃ ┣ 📜combined_metrics.png
 ┃ ┃ ┃ ┣ 📜comparison_epsilon_decay.png
 ┃ ┃ ┃ ┣ 📜comparison_rewards.png
 ┃ ┃ ┃ ┣ 📜convergence_speed.png
 ┃ ┃ ┃ ┣ 📜learning_progress.png
 ┃ ┃ ┃ ┗ 📜stability_rewards.png
 ┃ ┃ ┣ 📂LunarLander-v3_ENV
 ┃ ┃ ┃ ┣ 📜checkpoint_comparison.png
 ┃ ┃ ┃ ┣ 📜checkpoint_summary.csv
 ┃ ┃ ┃ ┣ 📜combined_metrics.png
 ┃ ┃ ┃ ┣ 📜comparison_epsilon_decay.png
 ┃ ┃ ┃ ┣ 📜comparison_rewards.png
 ┃ ┃ ┃ ┣ 📜convergence_speed.png
 ┃ ┃ ┃ ┣ 📜learning_progress.png
 ┃ ┃ ┃ ┗ 📜stability_rewards.png
 ┃ ┃ ┣ 📂MountainCar-v0_ENV
 ┃ ┃ ┃ ┣ 📜checkpoint_comparison.png
 ┃ ┃ ┃ ┣ 📜checkpoint_summary.csv
 ┃ ┃ ┃ ┣ 📜combined_metrics.png
 ┃ ┃ ┃ ┣ 📜comparison_epsilon_decay.png
 ┃ ┃ ┃ ┣ 📜comparison_rewards.png
 ┃ ┃ ┃ ┣ 📜convergence_speed.png
 ┃ ┃ ┃ ┣ 📜learning_progress.png
 ┃ ┃ ┃ ┗ 📜stability_rewards.png
 ┃ ┃ ┣ 📜DDQN_Alog.png
 ┃ ┃ ┣ 📜dqn_algo.png
 ┃ ┃ ┣ 📜Dueling_dqn.png
 ┃ ┃ ┗ 📜model_code_snippet.jpeg
 ┃ ┣ 📂videos
 ┃ ┃ ┣ 📂DoubleDQN
 ┃ ┃ ┃ ┣ 📂CartPole-v1
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┃ ┣ 📂LunarLander-v3
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┃ ┗ 📂MountainCar-v0
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┣ 📂DQN
 ┃ ┃ ┃ ┣ 📂CartPole-v1
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┃ ┣ 📂LunarLander-v3
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┃ ┗ 📂MountainCar-v0
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┗ 📂DuelingDQN
 ┃ ┃ ┃ ┣ 📂CartPole-v1
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┃ ┣ 📂LunarLander-v3
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┃ ┃ ┗ 📂MountainCar-v0
 ┃ ┃ ┃ ┃ ┗ 📜best_strategy.mp4
 ┃ ┣ 📂weights
 ┃ ┃ ┣ 📂DoubleDQN
 ┃ ┃ ┃ ┣ 📂CartPole-v1
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┃ ┣ 📂LunarLander-v3
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┃ ┗ 📂MountainCar-v0
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┣ 📂DQN
 ┃ ┃ ┃ ┣ 📂CartPole-v1
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┃ ┣ 📂LunarLander-v3
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┃ ┗ 📂MountainCar-v0
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┗ 📂DuelingDQN
 ┃ ┃ ┃ ┣ 📂CartPole-v1
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┃ ┣ 📂LunarLander-v3
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┃ ┃ ┗ 📂MountainCar-v0
 ┃ ┃ ┃ ┃ ┣ 📜1000.pth
 ┃ ┃ ┃ ┃ ┣ 📜2000.pth
 ┃ ┃ ┃ ┃ ┣ 📜best.pth
 ┃ ┃ ┃ ┃ ┗ 📜training.log
 ┃ ┣ 📜compare_checkpoints.py
 ┃ ┗ 📜visualize_comparison.py
 ┣ 📂utils
 ┃ ┣ 📜logger.py
 ┃ ┗ 📜video_recorder.py
 ┣ 📜base_dqn.py
 ┣ 📜data.py
 ┣ 📜double_dqn.py
 ┣ 📜dqn.py
 ┣ 📜dueling_dqn.py
 ┣ 📜main.py
 ┣ 📜memory.py
 ┣ 📜models.py