This project provides an AI research environment for Atari 2600 games, built on OpenAI's Atari Gym. It's designed for implementing and comparing various Reinforcement Learning (RL) algorithms, targeting AI researchers and practitioners interested in RL. The environment allows for standardized benchmarking of RL approaches against a diverse set of classic Atari titles.

How It Works

The environment leverages OpenAI's Atari Gym as its foundation, providing a consistent interface to a wide array of Atari games. It implements a Deep Convolutional Neural Network (CNN) architecture, inspired by DeepMind's work, for processing game states. This CNN architecture, featuring multiple convolutional layers followed by dense layers, is optimized for extracting relevant features from pixel-based game inputs, enabling effective learning for RL agents.

Quick Start & Requirements

• Install via pip install -r requirements.txt within the cloned repository.
• Requires Python and standard ML dependencies.
• Launch via python atari.py --help for available modes.
• Official Medium article: Atari - Reinforcement Learning in depth (Part 1: DDQN)

Highlighted Details

• Implements Double Deep Q-Network (DDQN) and Genetic Evolution (GE) algorithms.
• Demonstrates significant performance gains over human averages in games like Space Invaders (128%) and Breakout (221%) using DDQN.
• Features a DeepMind-inspired CNN architecture with 1.68M trainable parameters.
• Supports a comprehensive list of 60+ Atari 2600 games.

Maintenance & Community

• Developed by Greg Surma.
• Links to author's portfolio and GitHub are provided.

Licensing & Compatibility

• The README does not explicitly state a license.

Limitations & Caveats

The project's licensing is not specified, which may pose compatibility issues for commercial or closed-source applications. The README also does not detail specific hardware requirements beyond mentioning GPU performance for training times.