Conductance and the rapid mixing property for Markov chains: the approximation of permanent resolved
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
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
Analyzing Kleinberg's (and other) small-world Models
Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
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Understanding how we are able to perform a diverse set of complex tasks is a central question for the Artificial Intelligence community. A popular approach is to use temporal abstraction as a framework to capture the notion of subtasks. However, this transfers the problem to finding the right subtasks, which is still an open problem. Existing approaches for subtask generation require too much knowledge of the environment, and the abstractions they create can overwhelm the agent. We propose a simple algorithm inspired by small world networks to learn subtasks while solving a task that requires virtually no information of the environment. Additionally, we show that the subtasks we learn can be easily composed by the agent to solve any other task; more formally, we prove that any task can be solved using only a logarithmic combination of these subtasks and primitive actions. Experimental results show that the subtasks we generate outperform other popular subtask generation schemes on standard domains.