Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

🚀 Course Title: Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

🎓 Course Headline: Master Deep Reinforcement Learning to Beat Classic Atari Games with Deep Q Networks! 🕹️🧠

Unlock the Secrets of Deep Reinforcement Learning with Modern Research Papers and Implement Them from Scratch using PyTorch & TensorFlow 2.0!

Welcome to an in-depth journey into the world of Deep Reinforcement Learning (DRL)! This course is designed for learners who are eager to bridge the gap between academic research and practical implementation. By the end of this course, you'll not only understand the concepts behind the curtain of DRL but also be able to turn them into agents that can master classic Atari games. 🎮

Course Outline:

📚 Understanding Deep Q Learning Algorithms:

• Dive deep into the papers that introduced key algorithms like Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning.
• Gain insights into the inner workings of these algorithms through clear explanations and step-by-step guidance.

Implementation with PyTorch & TensorFlow 2:

• Learn how to code these algorithms using the powerful and intuitive libraries: PyTorch and TensorFlow 2.0.
• Master the art of translating research into code, making your agents ready to tackle any future DQL algorithms you wish to explore.

Tackling the Atari Challenges with OpenAI Gym:

• Modify the OpenAI Gym's Atari library to align with the conditions set by the original DRL papers.
  • Execute repetitive actions to minimize computation.
  • Rescale images for more efficient processing.
  • Stack frames to provide a sense of motion for the agent.
  • Introduce random no-ops to prevent model overtraining.
  • Clip rewards to ensure the agent generalizes well across different games.

Foundational Knowledge:

• No prior experience in reinforcement learning or deep learning? No problem! Start with a comprehensive introduction to reinforcement learning basics.
• Solve the Frozen Lake environment from the Open AI Gym to build your foundational understanding.
• Topics covered:
  • Markov decision processes (MDPs)
  • Temporal difference learning
  • The Q learning algorithm
  • Solving the Bellman equation
  • Value functions and action value functions
  • Dive into model-free vs. model-based RL
  • Explore solutions to the explore-exploit dilemma, such as optimistic initial values and epsilon-greedy action selection.

Deep Learning with PyTorch:

• A mini-course tailored for those with basic deep learning knowledge or familiarity with other frameworks like Tensorflow or Keras.
• Learn to code deep neural networks in PyTorch, understand the functioning of convolutional neural networks (CNNs), and apply this knowledge to implement a naive DQL agent to solve the Cartpole problem from the Open AI Gym.

Who is this course for?

• Aspiring data scientists, AI enthusiasts, researchers, students, and professionals interested in deep reinforcement learning and its applications in various domains.
• Those looking to solidify their understanding of DRL concepts through practical implementation and problem-solving with real-world examples.

What will you gain?

• A comprehensive understanding of how to read, implement, and optimize deep reinforcement learning algorithms from research papers.
• Proficiency in coding these algorithms using PyTorch and TensorFlow 2.0.
• The ability to apply your knowledge to solve complex environments in the Open AI Gym, specifically classic Atari games.
• A solid foundation in both reinforcement learning and deep learning that will empower you to tackle a wide range of challenges in AI.

Embark on this transformative learning experience today and join the ranks of those who have turned theoretical research into practical, beating-the-classics success stories! 🏆✨

Enroll Now and Transform Your Data Science Skills with Modern Deep Reinforcement Learning! 📊🚀