Abstract
Employing a learning system in which the "actor-critic' reinforcement learning method and a Gauss-sigmoid neural network are combined, a high performance learning system can be built due to the combination of the autonomy for reinforcement learning and the generalization capability for neural networks. Much time is needed when the conventional reinforcement learning method is used in order to train a robot with huge number of states such as our 1-legged hopping robot with 3-DOF. In this study, based on a simulation study for a wheel type inverted pendulum, we examined a few learning techniques different from conventional methods from the viewpoint of the improvement of learning speed. The obtained result shows that the increase of the number of modification steps as for connection strengths in neural networks is very effective for the decrease of learning cost.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
