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.
🧠 Key Learning Outcomes
- Grasp the mathematical framework of RL.
- Implement algorithms using Python and TensorFlow.
- Analyze case studies in robotics and game theory.
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.
Join our community to discuss RL challenges and solutions: /en/forums/rl-discussion.
Note: All images and links are illustrative. Replace placeholders with actual content as needed.