Modern Reinforcement Learning: Actor-Critic Agents

🤖 Modern Reinforcement Learning: Actor-Critic Agents with PyTorch & TensorFlow

🚀 Course Overview: In this advanced course, you'll dive deep into the world of deep reinforcement learning (DRL) and master the implementation of cutting-edge algorithms in the OpenAI Gym. With a focus on tackling environments with continuous action spaces, this course is perfect for those looking to push the boundaries of AI in robotic control and beyond.

🧠 What You'll Learn:

• Implement policy gradient, actor-critic, DDPG, TD3, and SAC algorithms from scratch.
• Read and comprehend complex deep RL research papers on your own.
• Develop a repeatable framework for swift implementation of advanced DRL concepts.

🎓 Course Structure:

1. Core Reinforcement Learning Fundamentals:

   • The Bellman Equation, Markov Decision Processes, Monte Carlo methods, Temporal Difference (TD) methods, and more.

2. Hands-On Projects:

   • Coding a blackjack-playing AI to review basics.
   • Balancing a cart pole using Q Learning.

3. Policy Gradient Methods:

   • Implement REINFORCE for the Lunar Lander environment.
   • Code up the one-step actor critic algorithm for the same challenge.

4. Deep Reinforcement Learning Algorithms:

   • Deep Deterministic Policy Gradients (DDPG) to tackle continuous control tasks.
   • Implement Twin Delayed Deep Deterministic Policy Gradients (TD3) for world-class performance in the Bipedal Walker environment.
   • Soft Actor Critic (SAC) to maximize entropy and achieve exceptional results across multiple OpenAI Gym environments.

🧐 Key Takeaways: By the end of this course, you'll be able to:

• Understand why actor-critic methods are valuable in DRL.
• Apply advances from deep Q learning to other areas within RL.
• Resolve the explore-exploit dilemma with a deterministic policy.
• Address overestimation bias and approximation errors effectively.

🎫 Prerequisites: This course is not for beginners! You must have:

• A solid understanding of college-level calculus.
• A foundation in reinforcement learning concepts.
• Knowledge of deep learning techniques using PyTorch or TensorFlow.

🔥 Why Take This Course?

• Learn to implement advanced DRL algorithms directly from research papers.
• Stay ahead of the curve with a curriculum that aligns with state-of-the-art RL research.
• Avoid the pitfalls of outdated medium blog posts and other unreliable sources.
• Master a repeatable, industry-standard framework for turning research into code.

🚀 Your Journey to Mastering DRL: Join us on this exciting journey through the depths of deep reinforcement learning. With the guidance of instructor Phil Tabor, you'll emerge not just as a practitioner but as a leader in AI engineering. Enroll now and take your first step towards mastering modern DRL techniques! 🎓🚀