Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Control
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Continual Robot Learning with Constructive Neural Networks
EWLR-6 Proceedings of the 6th European Workshop on Learning Robots
Action Chaining by a Developmental Robot with a Value System
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Interactive robot task training through dialog and demonstration
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Fitted Natural Actor-Critic: A New Algorithm for Continuous State-Action MDPs
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Using continuous action spaces to solve discrete problems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Applying neural network to reinforcement learning in continuous spaces
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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The full deployment of service robots in daily activities will require the robot to adapt to the needs of non-expert users, particularly, to learn how to perform new tasks from "natural" interactions. Reinforcement learning has been widely used in robotics, however, traditional algorithms require long training times, and may have problems with continuous spaces. Programming by demonstration has been used to instruct a robot, but is limited by the quality of the trace provided by the user. In this paper, we introduce a novel approach that can handle continuous spaces, can produce continuous actions and incorporates the user's intervention to quickly learn optimal policies of tasks defined by the user. It is shown how the continuous actions produce smooth trajectories and how the user's intervention allows the robot to learn significantly faster optimal policies. The proposed approach is tested in a simulated robot with very promising results.