Title :
Supplementing neural reinforcement learning with symbolic methods: Possibilities and challenges
Author_Institution :
NEC Res. Inst., Princeton, NJ, USA
Abstract :
Several different ways of using symbolic methods to improve reinforcement learning are identified and discussed in some detail. Each demonstrates to some extent the advantages of combining reinforcement learning and symbolic methods. These methods point to the potentials and the challenges of this line of research
Keywords :
formal logic; learning (artificial intelligence); neural nets; RL; neural reinforcement learning; symbolic methods; Decision making; Learning systems; National electric code; Neural networks; Partitioning algorithms; Process planning; Space exploration; State-space methods; Sun; Usability;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830828