Welcome to the Reinforcement Learning (RL) course! This is a fascinating field of machine learning where agents learn to make decisions by interacting with an environment. Here's a quick overview to get you started:

🔍 Key Concepts

  • Agent-Environment Interaction: Agents take actions to maximize cumulative rewards.
  • Reward Signals: The core mechanism driving learning behavior.
  • Markov Decision Processes (MDPs): Framework for modeling sequential decision problems.
Reinforcement Learning Overview

🧠 Popular Algorithms

  1. Q-Learning: A model-free algorithm for learning optimal actions.
  2. Deep Q-Networks (DQNs): Combines Q-learning with deep neural networks.
  3. Policy Gradients: Directly optimizes policies using gradient ascent.
Deep Q Networks

🌍 Real-World Applications

  • Robotics: Teaching robots to navigate or manipulate objects.
  • Game Theory: AI strategies in games like chess or Go.
  • Autonomous Vehicles: Decision-making in dynamic environments.

For deeper exploration, check out our related course on Machine Learning Fundamentals. Happy learning! 🎓