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How to Reduce Action Space for Planning Domains? (Student Abstract)
2022
AAAI Conference on Artificial Intelligence
While AI planning and Reinforcement Learning (RL) solve sequential decision-making problems, they are based on different formalisms, which leads to a significant difference in their action spaces. When solving planning problems using RL algorithms, we have observed that a naive translation of the planning action space incurs severe degradation in sample complexity. In practice, those action spaces are often engineered manually in a domain-specific manner. In this abstract, we present a method
dblp:conf/aaai/Kokel00SS22
fatcat:4hr5woukwramzakn5f5g5l4ipq