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arxiv: 1206.6405 · v1 · pith:JQ2W2XANnew · submitted 2012-06-27 · 💻 cs.LG · cs.AI· stat.ML

Bounded Planning in Passive POMDPs

classification 💻 cs.LG cs.AIstat.ML
keywords boundedpassivepomdpsactionsaffectagentalgorithmapproximation
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In Passive POMDPs actions do not affect the world state, but still incur costs. When the agent is bounded by information-processing constraints, it can only keep an approximation of the belief. We present a variational principle for the problem of maintaining the information which is most useful for minimizing the cost, and introduce an efficient and simple algorithm for finding an optimum.

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  1. Principles of frugal inference and control

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    Introduces a resource-constrained POMDP framework and derives three principles of frugal inference and control that generalize to nonlinear tasks like pole balancing.