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To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction.
Optimal control of Markov decision processes with incomplete state estimation
Karl J Astrom · 1965
Earlier work this paper cites.
Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
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Reinforcement learning: An introduction , volume 1
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
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