Dec-POMDPs as Non-Observable MDPs
نویسندگان
چکیده
A recent insight in the field of decentralized partially observable Markov decision processes (Dec-POMDPs) is that it is possible to convert a Dec-POMDP to a non-observable MDP, which is a special case of POMDP. This technical report provides an overview of this reduction and pointers to related literature.
منابع مشابه
Probabilistic Planning with Risk-Sensitive Criterion
Probabilistic planning models and, in particular, Markov Decision Processes (MDPs), Partially Observable Markov Decision Processes (POMDPs) and Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) have been extensively used by AI and Decision Theoretic communities for planning under uncertainty. Typically, the solvers for probabilistic planning models find policies that min...
متن کاملProducing efficient error-bounded solutions for transition independent decentralized mdps
There has been substantial progress on algorithms for single-agent sequential decision making using partially observable Markov decision processes (POMDPs). A number of efficient algorithms for solving POMDPs share two desirable properties: error-bounds and fast convergence rates. Despite significant efforts, no algorithms for solving decentralized POMDPs benefit from these properties, leading ...
متن کاملFiltered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs
Potential games and decentralised partially observable MDPs (Dec–POMDPs) are two commonly used models of multi–agent interaction, for static optimisation and sequential decision– making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player’s observations of others’ actions are perturbed by random noise, and use this...
متن کاملExploiting separability in multiagent planning with continuous-state MDPs
Recent years have seen significant advances in techniques for optimally solving multiagent problems represented as decentralized partially observable Markov decision processes (Dec-POMDPs). A new method achieves scalability gains by converting Dec-POMDPs into continuous state MDPs. This method relies on the assumption of a centralized planning phase that generates a set of decentralized policie...
متن کاملAutomated Generation of Interaction Graphs for Value-Factored Dec-POMDPs
The Decentralized Partially Observable Markov Decision Process (Dec-POMDP) is a powerful model for multiagent planning under uncertainty, but its applicability is hindered by its high complexity – solving Dec-POMDPs optimally is NEXP-hard. Recently, Kumar et al. introduced the Value Factorization (VF) framework, which exploits decomposable value functions that can be factored into subfunctions....
متن کامل