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Composable Planning with Attributes
[article]
2019
arXiv
pre-print
The tasks that an agent will need to solve often are not known during training. However, if the agent knows which properties of the environment are important then, after learning how its actions affect those properties, it may be able to use this knowledge to solve complex tasks without training specifically for them. Towards this end, we consider a setup in which an environment is augmented with a set of user defined attributes that parameterize the features of interest. We propose a method
arXiv:1803.00512v2
fatcat:wt5f7qrrljdq5pdyc7xxxr3gaa