Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Real-world reinforcement learning for autonomous humanoid robot charging in a home environment
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Robotics and Autonomous Systems
Mobile robot path planning using polyclonal-based artificial immune network
Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
Hi-index | 0.00 |
Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state space, common in reinforcement learning, makes it difficult to be used in real-world scenarios. In this work we describe a novel real-world reinforcement learning method. It uses a supervised reinforcement learning approach combined with Gaussian distributed state activation. We successfully tested this method in two real scenarios of humanoid robot navigation: first, backward movements for docking at a charging station and second, forward movements to prepare grasping. Our approach reduces the required learning steps by more than an order of magnitude, and it is robust and easy to be integrated into conventional RL techniques.