Tutorial_12_Deep_Reinforcement_Learning

Deep Reinforcement Learning

This repository contains two tutorials on Deep Reinforcement Learning (DRL), showcasing implementations in TensorFlow and PyTorch. These tutorials are designed to guide you through the foundational concepts of DRL, accompanied by practical coding examples.

Tutorials Overview

1. TensorFlow Implementation

• Notebook: Tutorial#12 (tf) Deep Reinforcement Learning.ipynb
• Description: This tutorial uses TensorFlow to implement key DRL techniques. It includes examples on:
  • Defining the environment and agent.
  • Implementing Q-learning and deep Q-networks (DQN).
  • Training and evaluating the model.
• Prerequisites: Familiarity with TensorFlow, Python, and basic reinforcement learning concepts.

2. PyTorch Implementation

• Notebook: Tutorial#12 (torch) Deep Reinforcement Learning.ipynb
• Description: This tutorial demonstrates DRL using PyTorch. It covers:
  • Setting up the environment and agent.
  • Using Q-learning and deep Q-networks (DQN).
  • Training, debugging, and performance tuning.
• Prerequisites: Basic knowledge of PyTorch, Python, and reinforcement learning.

Learning Objectives

• Understand the basics of Deep Reinforcement Learning.
• Implement DRL models using TensorFlow and PyTorch.
• Train agents in simulated environments and analyze their performance.

Resources

• OpenAI Gym Documentation
• Deep Reinforcement Learning Papers and Guides

Acknowledgments

• François Chollet
• Tensorflow.org
• pytorch.org