This repository provides comprehensive materials for Udacity's Deep Reinforcement Learning Nanodegree program, targeting students and practitioners seeking to implement and understand various RL algorithms. It offers hands-on tutorials and projects using PyTorch and Unity ML-Agents, covering foundational concepts to advanced deep RL techniques.

How It Works

The project is structured around implementing core RL algorithms, including Dynamic Programming, Monte Carlo, Temporal-Difference methods (Sarsa, Q-Learning), and deep RL approaches like Deep Q-Networks (DQN) and Deep Deterministic Policy Gradients (DDPG). It utilizes PyTorch (v0.4) for deep learning components and integrates with OpenAI Gym for classic control tasks and Unity ML-Agents for robotics simulations, enabling practical application and benchmarking.

Quick Start & Requirements

• Install: Clone the repo, create a Python 3.6 environment (conda create -n drlnd python=3.6), activate it, install OpenAI Gym and its dependencies (classic control, box2d), navigate to deep-reinforcement-learning/python, and run pip install .. An IPython kernel for the environment is also recommended.
• Prerequisites: Python 3.6, Conda, OpenAI Gym, Unity ML-Agents, PyTorch v0.4. Windows users require "Build Tools for Visual Studio 2019".
• Resources: Official documentation and setup guides for OpenAI Gym are linked.

Highlighted Details

• Implements a wide range of RL algorithms from basic DP to advanced DDPG.
• Features practical projects using Unity ML-Agents for robotics and game simulations.
• Includes benchmarks and performance metrics for various algorithms on OpenAI Gym environments.
• Provides a cheatsheet for guided study.

Maintenance & Community

This repository is associated with Udacity's Nanodegree program. Specific community channels or active maintenance status are not detailed in the README.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README. Compatibility for commercial use or closed-source linking would require clarification of the license.

Limitations & Caveats

The code is based on PyTorch v0.4, which is an older version and may require compatibility adjustments for current PyTorch installations. Some features are marked as "Coming soon!" indicating incomplete implementation.