Welcome to the Reinforcement Learning (RL) course! This program is designed to equip you with the foundational knowledge and practical skills to master RL techniques.

📘 Course Overview

  • Basics of RL Theory: Understand the core concepts like agents, environments, rewards, and policies.
  • Algorithms & Implementation: Dive into Q-learning, Deep Q-Networks (DQN), and Policy Gradients.
  • Project Applications: Apply RL to real-world problems such as robotics, game strategies, and autonomous systems.
Reinforcement_Learning

🧠 Key Learning Outcomes

  • Grasp the mathematical framework of RL.
  • Implement algorithms using Python and TensorFlow.
  • Analyze case studies in robotics and game theory.
Q_Learning

For advanced topics, explore our dedicated section: /en/courses/rl/advanced.

🤖 Practical Projects

  • Robotics Navigation: Train agents to navigate dynamic environments.
  • Game Strategy Optimization: Develop AI for board games or simulations.
  • Reinforcement Learning in Finance: Apply RL to trading algorithms.
Robotics_Application

Join our community to discuss RL challenges and solutions: /en/forums/rl-discussion.


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