Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning Options in Reinforcement Learning
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Temporal abstraction in reinforcement learning
Temporal abstraction in reinforcement learning
Dynamic abstraction in reinforcement learning via clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fast gradient-descent methods for temporal-difference learning with linear function approximation
ICML '09 Proceedings of the 26th Annual 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
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multi-timescale nexting in a reinforcement learning robot
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Intelligence can be defined, informally, as knowing a lot and being able to use that knowledge flexibly to achieve one's goals. In this sense it is clear that knowledge is central to intelligence. However, it is less clear exactly what knowledge is, what gives it meaning, and how it can be efficiently acquired and used. In this talk we re-examine aspects of these age-old questions in light of modern experience (and particularly in light of recent work in reinforcement learning). Such questions are not just of philosophical or theoretical import; they directly effect the practicality of modern knowledge-based systems, which tend to become unwieldy and brittle--difficult to change--as the knowledge base becomes large and diverse.