PyTorch Reinforcement Learning Tutorials 🏭🤖

Welcome to the PyTorch reinforcement learning tutorials! Here, you'll find comprehensive guides to help you get started with reinforcement learning (RL) using PyTorch. Whether you're a beginner or an experienced researcher, these resources will provide valuable insights.

🧠 Key Topics Covered

• Basics of RL

  • Introduction to agents, environments, and reward systems
  • Q-learning vs. Policy Gradients
  • Explore RL Fundamentals

• Advanced Techniques

  • Deep Q-Networks (DQN) with experience replay
  • Proximal Policy Optimization (PPO)
  • Multi-agent systems and distributed training

• Practical Examples

  • CartPole balancing task 🚗
  • Maze navigation with neural networks 🧭
  • Check out the CartPole Tutorial

📚 Recommended Reading

For a deeper dive into PyTorch's RL capabilities:

1. PyTorch Lightning for RL
2. RL in PyTorch: A Practical Guide
3. Reinforcement Learning Algorithms

📷 Visual Aids

*Figure 1: Overview of reinforcement learning concepts* *Figure 2: Deep Q-Network architecture* *Figure 3: Policy Gradient training process*

Let us know if you'd like to explore specific topics further! 🚀