Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks
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 MFRL approaches. This could drastically reduce the training time of MFRL, and increase the application of MFRL for real-world robotic tasks. This article presents a study on the feasibility of using the state-of-the-art MBRL to improve the training time for two real-world dexterous manipulation tasks. The evaluation is conducted on a real low-cost robot gripper where the predictive model and the control policy are learned from scratch. The results indicate that MBRL is capable of learning accurate models of the world, but does not show clear improvements in learning the control policy as prior literature suggests should be expected.
You can find all the code associated with this project at the following URL: https://github.com/dvalenciar/MBRL_MFRL_Rotation_Paper