A Concise Introduction to Decentralized POMDPs by Frans A. Oliehoek, Christopher Amato

By Frans A. Oliehoek, Christopher Amato

This e-book introduces multiagent making plans below uncertainty as formalized via decentralized in part observable Markov choice strategies (Dec-POMDPs). The meant viewers is researchers and graduate scholars operating within the fields of synthetic intelligence concerning sequential choice making: reinforcement studying, decision-theoretic making plans for unmarried brokers, classical multiagent making plans, decentralized regulate, and operations learn.

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It is straightforward to see that in this case, the problem can be decomposed into n separate MDPs and their solution can then be combined. When only the transitions and observations are independent, the problem becomes NPcomplete. Intuitively, this occurs because the other agents’ policies do not affect an agent’s state (only the reward attained at the set of local states). Because independent transitions and observations imply local full observability, an agent’s observation history does not provide any additional information about its own state—it is already known.

3) If this is the case, the global reward is maximized by maximizing local rewards. 4) i∈D are frequently used. 3 Centralized Models: MMDPs and MPOMDPs In the discussion so far we have focused on models that, in the execution phase, are truly decentralized: they model agents that select actions based on local observations. , in which (joint) actions can be selected based on global information. Such global information can arise due to either full observability or communication. In the former case, each agent simply observes the same observation or state.

Such global information can arise due to either full observability or communication. In the former case, each agent simply observes the same observation or state. In the latter case, we have to assume that agents can share their individual observations over an instantaneous and noise-free communication channel without costs. In either case, this allows the construction of a centralized model. For instance, under such communication, a Dec-MDP effectively reduces to a multiagent Markov decision process (MMDP) introduced by Boutilier [1996].

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