Artificial Intelligence Review - Special issue on lazy learning
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Imitation in animals and artifacts
Imitation in animals and artifacts
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
ECML'05 Proceedings of the 16th European conference on Machine Learning
Automatically composing and parameterizing skills by evolving Finite State Automata
Robotics and Autonomous Systems
Robot learning from demonstration by constructing skill trees
International Journal of Robotics Research
Unified inter and intra options learning using policy gradient methods
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Curriculum learning for motor skills
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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Abstraction of complex, longer motor tasks into simpler elemental movements enables humans and animals to exhibit motor skills which have not yet been matched by robots. Humans intuitively decompose complex motions into smaller, simpler segments. For example when describing simple movements like drawing a triangle with a pen, we can easily name the basic steps of this movement. Surprisingly, such abstractions have rarely been used in artificial motor skill learning algorithms. These algorithms typically choose a new action (such as a torque or a force) at a very fast time-scale. As a result, both policy and temporal credit assignment problem become unnecessarily complex - often beyond the reach of current machine learning methods. We introduce a new framework for temporal abstractions in reinforcement learning (RL), i.e. RL with motion templates. We present a new algorithm for this framework which can learn high-quality policies by making only few abstract decisions.