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The Eigenoption-Critic Framework
[article]
2017
arXiv
pre-print
Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration. Despite its initial promising results, a couple of issues in current algorithms limit its application, namely: (1) EO methods require two separate steps (eigenoption discovery and reward maximization) to learn a control policy, which can incur a significant amount of storage and computation; (2) EOs are only
arXiv:1712.04065v1
fatcat:slkplzgaczeyljtp532plykeiq