Deep Reinforcement Learning Course
Gain expertise in deep reinforcement learning for robotics applications. This course teaches sim-to-real transfer methods, safe control strategies, reward engineering, and reliable policies with cutting-edge DRL algorithms. Ideal for engineers and professionals implementing smart systems in manufacturing environments. It covers designing simulated setups, algorithm selection like SAC, PPO, TD3, sensor integration, safety enforcement, and complete deployment workflows.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
This course offers a hands-on approach to creating and deploying strong RL policies for advanced robotic systems. Participants will build simulated environments, design reward functions, select algorithms such as SAC, PPO, and TD3, and develop vision and sensor fusion systems. Key topics include ensuring safety, handling sim-to-real gaps, monitoring performance metrics, and managing full evaluation and deployment processes.
Elevify advantages
Develop skills
- Design RL tasks for robots by defining states, actions, rewards, and safety measures.
- Build strong sim-to-real pipelines using domain randomisation and monitoring techniques.
- Engineer rewards and penalties to promote safe and efficient robot behaviour.
- Select and fine-tune DRL algorithms like PPO, SAC, TD3 for stable robot control.
- Set up sensors and state fusion for dependable perception in factory settings.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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