HQ-Learning
نویسندگان
چکیده
HQ-learning is a hierarchical extension of Q()-learning designed to solve certain types of partially observable Markov decision problems (POMDPs). HQ automatically decomposes POMDPs into sequences of simpler subtasks that can be solved by memoryless policies learn-able by reactive subagents. HQ can solve partially observable mazes with more states than those used in most previous POMDP work.
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ورودعنوان ژورنال:
- Adaptive Behaviour
دوره 6 شماره
صفحات -
تاریخ انتشار 1997