Introduction
Reinforcement Learning (RL) is a powerful approach that enables agents to learn optimal behaviors through interactions with an environment. This course introduces RL principles, including rewards, policies, and value functions. Participants will study famous RL algorithms and understand how they are applied in robotics, gaming, and decision-making systems. Hands-on coding exercises will help solidify key concepts. By the end, learners will be ready to explore more advanced RL techniques.
Course Objectives
- Learn RL terminology and components
- Understand exploration vs. exploitation
- Study major RL algorithms
- Build simple RL agents
- Explore real-world RL applications
Target Audience
- Intermediate AI learners
- ML engineers expanding skills
- Researchers interested in autonomous systems
- Robotics students
- Developers seeking advanced AI topics
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: RL Basics• Agents and environments
• State, action, reward
• Markov decision processes
• Episodic vs. continuous tasks
• Hands-on: Basic RL environment0 - Day 2: Value-Based Methods• Value functions
• Bellman equations
• Dynamic programming
• Temporal-Difference learning
• Hands-on: Implement TD learning0 - Day 3: Policy-Based Methods• Policies and gradients
• REINFORCE algorithm
• Advantages and limitations
• Stochastic vs. deterministic policies
• Hands-on: Policy gradient example0 - Day 4: Deep Reinforcement Learning• Deep Q-Networks (DQNs)
• Experience replay
• Target networks
• Stability challenges
• Hands-on: Build a DQN agent0 - Day 5: Applications & Trends• Robotics motion control
• Game-playing agents
• Multi-agent systems
• Safety considerations
• Capstone RL mini-project0
Instructor
Lessons:
