Temporal abstraction plays a key role in scaling up reinforcement learning algorithms. While learning and planning with given temporally extended actions has been well studied, the topic of how to construct this type of abstraction automatically from data is still open. We propose to to cluster the continuous state-interaction graph using community detection algorithms in order to construct extended actions, within the framework of options.
Pierre-Luc Bacon and Doina Precup. Using label propagation for learning temporally abstract
actions in reinforcement learning. In Proceedings of the Workshop on Multiagent Interaction Networks (MAIN 2013), 2013.
#License
Copyright (C) 2013 Pierre-Luc Bacon
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