A teaching method for reinforcement learning
ML92 Proceedings of the ninth international workshop on Machine learning
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Reinforcement Learning and Shaping: Encouraging Intended Behaviors
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Knowledge transfer via advice taking
Proceedings of the 3rd international conference on Knowledge capture
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Incorporating prior knowledge in support vector regression
Machine Learning
Towards reinforcement learning representation transfer
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Teachable robots: Understanding human teaching behavior to build more effective robot learners
Artificial Intelligence
Autonomous transfer for reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study
RoboCup 2006: Robot Soccer World Cup X
Connection Science - Social Learning in Embodied Agents
Learning about objects with human teachers
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
A simple and effective method for incorporating advice into kernel methods
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Autonomous inter-task transfer in reinforcement learning domains
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Transparent active learning for robots
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Relational macros for transfer in reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Building relational world models for reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Using spatial hints to improve policy reuse in a reinforcement learning agent
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Adaptation-based programming in java
Proceedings of the 20th ACM SIGPLAN workshop on Partial evaluation and program manipulation
A formal framework for combining natural instruction and demonstration for end-user programming
Proceedings of the 16th international conference on Intelligent user interfaces
Knowledge of opposite actions for reinforcement learning
Applied Soft Computing
Skill acquisition via transfer learning and advice taking
ECML'06 Proceedings of the 17th European conference on Machine Learning
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Faster program adaptation through reward attribution inference
Proceedings of the 11th International Conference on Generative Programming and Component Engineering
| Hi-index | 0.00 |
We present a novel formulation for providing advice to a reinforcement learner that employs support-vector regression as its function approximator. Our new method extends a recent advice-giving technique, called Knowledge-Based Kernel Regression (KBKR), that accepts advice concerning a single action of a reinforcement learner. In KBKR, users can say that in some set of states, an action's value should be greater than some linear expression of the current state. In our new technique, which we call Preference KBKR (Pref-KBKR), the user can provide advice in a more natural manner by recommending that some action is preferred over another in the specified set of states. Specifying preferences essentially means that users are giving advice about policies rather than Q values, which is a more natural way for humans to present advice. We present the motivation for preference advice and a proof of the correctness of our extension to KBKR. In addition, we show empirical results that our method can make effective use of advice on a novel reinforcement-learning task, based on the RoboCup simulator, which we call Breakaway. Our work demonstrates the significant potential of advice-giving techniques for addressing complex reinforcement learning problems, while further demonstrating the use of support-vector regression for reinforcement learning.