Relational State-Space Feature Learning and Its Applications in Planning
نویسندگان
چکیده
We consider how to learn useful relational features in linear approximated value function representations for solving probabilistic planning problems. We first discuss a current feature-discovering planner that we presented at the International Conference on Automated Planning and Scheduling (ICAPS) in 2007. We then propose how the feature learning framework can be further enhanced to improve problem
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