https://github.com/paraskevi-kivroglou/rl-pong-agent
A project by Paraskevi Kivroglou as part of exploring deep reinforcement learning applications.
https://github.com/paraskevi-kivroglou/rl-pong-agent
atari atari-games gym-environment gymnasium numpy python3 pytorch q-learning reinforcement-learning reinforcement-learning-agent
Last synced: 3 months ago
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A project by Paraskevi Kivroglou as part of exploring deep reinforcement learning applications.
- Host: GitHub
- URL: https://github.com/paraskevi-kivroglou/rl-pong-agent
- Owner: Paraskevi-KIvroglou
- License: apache-2.0
- Created: 2024-06-23T23:27:11.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-10-28T22:54:30.000Z (7 months ago)
- Last Synced: 2025-01-23T13:45:59.455Z (4 months ago)
- Topics: atari, atari-games, gym-environment, gymnasium, numpy, python3, pytorch, q-learning, reinforcement-learning, reinforcement-learning-agent
- Language: Jupyter Notebook
- Homepage:
- Size: 92.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Q-Learning Agent for Atari Pong 🏓
A deep reinforcement learning implementation that masters the classic Atari game Pong using PyTorch and Deep Q-Learning.
🎯 Project Overview
This project implements a Deep Q-Learning Neural Network (DQN) that learns to play Atari's Pong from scratch. The agent learns by interacting with the game environment, developing strategies to defeat the built-in AI opponent.
🚀 Features
Deep Q-Learning implementation using PyTorch
Frame preprocessing for efficient learning
Experience replay buffer for improved training stability
Epsilon-greedy exploration strategy
Performance metrics tracking and visualization
Trained model checkpoints
🛠️ Prerequisites
-Python 3.8+
-PyTorch
-OpenAI Gym[atari]
-NumPy
-Matplotlib
This project is part of a Reinforcement Learning course I took during my Master's in Artificial Intelligence at IU. Here, you will find the code and files for a reinforcement learning agent playing pong atari.
This project is self-funded. I was not able to complete the training due to the cost of computing resources. If anyone has further suggestions or further feedback, please send me an email
at [email protected]
If you have a problem installing the OpenCV library in a Jupyter Notebook, try: !pip install opencv-python-headless