Learning hierarchical control structures for multiple tasks and changing environments
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
PolicyBlocks: An Algorithm for Creating Useful Macro-Actions in Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Discovering Hierarchy in Reinforcement Learning with HEXQ
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Using relative novelty to identify useful temporal abstractions in reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dynamic abstraction in reinforcement learning via clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Identifying useful subgoals in reinforcement learning by local graph partitioning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Automatic skill acquisition in reinforcement learning using graph centrality measures
Intelligent Data Analysis
| Hi-index | 0.00 |
We present a method for automatically creating a set of useful temporally-extended actions, or skills, in reinforcement learning. Our method identifies states that allow the agent to transition to a different region of the state space—for example, a doorway between two rooms—and generates temporally-extended actions that efficiently take the agent to these states. In identifying such states we use the concept of relative novelty, a measure of how much short-term novelty a state introduces to the agent. The resulting algorithm is simple, has low computational complexity, and is shown to improve performance in a number of problems.