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Markov decision processes (MDPs) are often used to model sequential decision problems involving uncertainty under the assumption of centralized control. However, many large, distributed systems do not permit centralized control due to communication limitations (such as cost, latency or corruption). This paper surveys recent work on decentralized control of MDPs in which control of each agent depends on a partial view of the world. We focus on a general framework where there may be uncertaintydoi:10.1109/cdc.2013.6760239 dblp:conf/cdc/AmatoCGUK13 fatcat:fe5yksf4zjfnrmhswb2rspipji