Welcome to your ultimate guide on getting started with Deep Reinforcement Learning (DRL) using PyTorch! If you’ve ever wanted to develop intelligent agents that can learn how to interact with their environment using various algorithms, you’re in the right place. This guide will walk you step by step through the installation and some crucial concepts. So, let’s get started!

Table of Contents

• 00. Prerequisites
• 01. Deep Learning with PyTorch
• 02. Deep Q-Network (DQN) & Double DQN (DDQN)
• 03. Advantage Actor-Critic (A2C) & Deep Deterministic Policy Gradient (DDPG)
• 04. Trust Region Policy Optimization (TRPO) & Proximal Policy Optimization (PPO)
• 05. Soft Actor-Critic (SAC)
• Learning Curves
• Additional Resources

00. Prerequisites

Before diving into DRL, you’ll need to set up your environment. Here are the requirements:

• Install from Anaconda to OpenAI Gym (Windows or MacOS)
• Numpy Installation

01. Deep Learning with PyTorch

Now that your environment is set up, you can start learning about Deep Learning with PyTorch. Here are some valuable resources:

• Lecture Slides
• Sample Code

02. Deep Q-Network (DQN) & Double DQN (DDQN)

Dive into the world of Deep Q-Learning with the following resources:

• Lecture Slides
• DQN Code
• DDQN Code

03. Advantage Actor-Critic (A2C) & Deep Deterministic Policy Gradient (DDPG)

Explore these advanced algorithms next:

• A2C:
  • Lecture Slides
  • A2C Code
• DDPG:
  • Lecture Slides
  • DDPG Code

04. Trust Region Policy Optimization (TRPO) & Proximal Policy Optimization (PPO)

Next up is an exploration of policy optimization techniques:

• TRPO:
  • Lecture Slides
  • TRPO Code
  • GAE Slides
  • TRPO + GAE Code
• PPO:
  • PPO Code
  • PPO Slides
  • PPO + GAE Code

05. Soft Actor-Critic (SAC)

Finally, dig into the Soft Actor-Critic algorithm:

• Lecture Slides
• SAC Code

Learning Curves

Understanding the performance of your agents is crucial. Below are examples of the learning curves for the environments:

• 
• 

Additional Resources

For those looking to expand their learning further, here are some papers and references:

• Deep Q-Network (DQN)
• Double DQN (DDQN)
• Advantage Actor-Critic (A2C)
• Asynchronous Advantage Actor-Critic (A3C)
• Deep Deterministic Policy Gradient (DDPG)
• Trust Region Policy Optimization (TRPO)
• Generalized Advantage Estimator (GAE)
• Proximal Policy Optimization (PPO)
• Soft Actor-Critic (SAC)

Troubleshooting

If you encounter issues during installation or while running the code, consider checking the following:

• Ensure you have the correct version of PyTorch installed, preferably v0.4.1.
• Check for any dependency errors and resolve them by installing missing libraries.
• If problems persist, consult the GitHub repository for updates or open issues.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.