Reinforcement Learning & Robotics Foundations
Q-learning, policy gradients, multi-agent RL, simulation environments. From theory to robot control. This architect track is structured for adult learners who need practical, career-relevant depth without academic abstraction. Delivered as a live experience, the course combines guided milestones, implementation reviews, and applied exercises aligned with modern AI, engineering, and technical leadership work.
Price
$8,000
Tax included
Duration
12 weeks
Format
Live
Instructor
Leo Schneider
Q-learning, policy gradients, multi-agent RL, simulation environments. From theory to robot control.
Course Plan
Syllabus
Establish the mathematical and practical foundations of sequential decision making.
- Agents and environments
- Rewards
- Markov decision processes
Outcomes
Includes
- • 12 live weeks with RL experiments and simulation labs.
- • Environment design and reward shaping worksheets.
- • Policy optimization walkthroughs and debugging sessions.
- • Robotics translation notes from simulation to physical systems.
Instructor
Leo Schneider
Leo works across control systems and machine learning, helping engineers connect reinforcement learning theory to embodied systems.