Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks
David Valencia,John Jia,Raymond Li,Alex Hayashi,Megan Lecchi,Reuel Terezakis,Trevor Gee,Minas Liarokapis,Bruce A. MacDonald,Henry Williams,David Valencia,John Jia,Raymond Li,Alex Hayashi,Megan Lecchi,Reuel Terezakis,Trevor Gee,Minas Liarokapis,Bruce A. MacDonald,Henry Williams
Model Free Reinforcement Learning (MFRL) has shown significant promise for learning dexterous robotic manipulation tasks, at least in simulation. However, the high number of samples, as well as the long training times, prevent MFRL from scaling to complex real-world tasks. Model- Based Reinforcement Learning (MBRL) emerges as a potential solution that, in theory, can improve the data efficiency of...
