2012
NIPS
NeurIPS 2012
Learning Partially Observable Models Using Temporally Abstract Decision Trees
Abstract
This paper introduces timeline trees, which are partial models of partially observable environments. Timeline trees are given some specific predictions to make and learn a decision tree over history. The main idea of timeline trees is to use temporally abstract features to identify and split on features of key events, spread arbitrarily far apart in the past (whereas previous decision-tree-based methods have been limited to a finite suffix of history). Experiments demonstrate that timeline trees can learn to make high quality predictions in complex, partially observable environments with high-dimensional observations (e.g. an arcade game).
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Reinforcement Learning
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Trend Setter
— Agent Systems
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Keyword Pioneer
— high-dimensional observation
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Hot Topic Early Bird
— reinforcement learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Artificial Intelligence > Core AI > Planning
Machine Learning > Learning Types > Unsupervised Learning
Reinforcement Learning > Methods > Deep RL
Reinforcement Learning > Methods > Policy Learning
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Representation Learning