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Learning agents for uncertain environments (extended abstract)
1998
Proceedings of the eleventh annual conference on Computational learning theory - COLT' 98
This talk proposes a very simple "baseline architecture" for a learning agent that can handle stochastic, partially observable environments. The architecture uses reinforcement learning together with a method for representing temporal processes as graphical models. I will discuss methods for learning the parameters and structure of such representations from sensory inputs, and for computing posterior probabilities. Some open problems remain before we can try out the complete agent; more arise
doi:10.1145/279943.279964
dblp:conf/colt/Russell98
fatcat:bnv35r7dzfdzfdhch3qea6amdy