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Deep Reinforcement Learning Course

Deep Reinforcement Learning Course
from 4 to 360h flexible workload
valid certificate in your country

What will I learn?

This Deep Reinforcement Learning Course provides a practical approach to developing and implementing strong RL policies for intricate robotic setups. You will create simulated settings, design reward systems, select algorithms such as SAC, PPO, and TD3, and construct vision and sensor integration processes. Gain knowledge in ensuring safety, handling sim-to-real transitions, monitoring metrics, and executing dependable evaluation and deployment processes from start to finish.

Elevify advantages

Develop skills

  • Design RL tasks for robots: define states, actions, rewards, and safety.
  • Build robust sim-to-real pipelines with domain randomization and monitoring.
  • Engineer rewards and penalties that drive safe, efficient robotic behavior.
  • Select and tune DRL algorithms (PPO, SAC, TD3) for stable robot control.
  • Configure sensors and state fusion for reliable factory-floor perception.

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.
Workload: between 4 and 360 hours

What our students say

As suas aulas são perfeitas. Adquiri o pacote de um ano e, finalmente, tenho a oportunidade de acompanhar diversos temas do meu interesse sem precisar mudar de plataforma... agradeço por tudo o que fazem, já recomendei vocês a outras pessoas...
Giulio Carlo
Giulio CarloAluno de Marketing Digital
Eu gosto de como as lições são direto ao ponto e como eu consigo alterar capítulos e pular conteúdos que não preciso.
Mariana Ferres
Mariana FerresAluna de Fotografia
Gosto do conteúdo e do modo de apresentação e transcrição de vídeos, o que acelera o processo!
Luciana Alvarenga
Luciana AlvarengaAluna de Design de Unhas
A plataforma é rápida, simples de usar. A diversidade de conteúdo e os vídeos complementares ajudam muito no aprendizado.
Giulio Carlo
Giulio CarloAluno de Engenharia de Prompt

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