Reinforcement Learning with Pytorch

Artificial Intelligence (AI) is rapidly transforming our world, and Reinforcement Learning (RL) is at the forefront of this revolution. This course dives into the exciting world of Reinforcement Learning, a powerful AI technique poised to unlock General Artificial Intelligence (AGI). Discover how RL is driving breakthroughs in areas like robotics, healthcare, and game playing, achieving superhuman performance in complex domains.

Whether you're a beginner or looking to deepen your understanding, this course provides a practical, hands-on approach to mastering RL. We'll build your knowledge from the ground up, culminating in the ability to train an agent to learn like a human – directly from video input!

What you'll learn:

• Introduction to Reinforcement Learning: Understand the core concepts and principles of RL.
• Markov Decision Processes (MDPs): Grasp the mathematical foundation of RL.
• Environments: Explore deterministic and stochastic environments and the Bellman Equation.
• Q-Learning: Learn a fundamental RL algorithm for optimal decision-making.
• Exploration vs. Exploitation: Master the crucial balance between exploring new actions and exploiting known rewards.
• Scaling Up: Understand techniques for scaling RL algorithms to handle complex problems.
• Neural Networks as Function Approximators: Discover how to use deep learning to represent complex policies.
• Deep Reinforcement Learning (DRL): Delve into the power of combining RL with deep neural networks.
• DQN (Deep Q-Network): Learn a foundational DRL algorithm for visual input.
• DQN Improvements: Explore advanced techniques to enhance DQN's performance.
• Learning from Video Input: Train agents to learn directly from raw video data.
• Reproducing Popular RL Solutions: Implement and understand state-of-the-art RL algorithms.
• Parameter Tuning & Recommendations: Gain practical insights into optimizing RL agents.

Throughout the course, we'll utilize the popular OpenAI Gym environment, progressing from basic text games to challenging Atari games. We'll emphasize practical application, focusing on why and how RL algorithms work, with updated code and installation instructions verified for PyTorch 1.6. Get ready to build intelligent agents and explore the future of AI!