Reinforcement learning for landmark-based robot navigation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Consistent Queries over Cardinal Directions Across Different Levels of Detail
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Spatial Abstraction: Aspectualization, Coarsening, and Conceptual Classification
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Representing and Selecting Landmarks in Autonomous Learning of Robot Navigation
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
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When a robot learns to solve a goal-directed navigation task with reinforcement learning, the acquired strategy can usually exclusively be applied to the task that has been learned. Knowledge transfer to other tasks and environments is a great challenge, and the transfer learning ability crucially depends on the chosen state space representation. This work shows how an agent-centered qualitative spatial representation can be used for generalization and knowledge transfer in a simulated robot navigation scenario. Learned strategies using this representation are very robust to environmental noise and imprecise world knowledge and can easily be applied to new scenarios, offering a good foundation for further learning tasks and application of the learned policy in different contexts.