Searching indoor environments in the presence of unknown obstacles with multiple UAV agents remains a challenge. This paper presents a framework for target-finding using a combination of traditional POMDP based planning and Deep Reinforcement Learning. The implementation breaks the problem into two separate stages of planning and control, with both stages modelled as a Partially Observable Markov Decision Process (POMDP). Global decentralised planning is provided using a modern online POMDP solver, while a modern Deep Reinforcement Learning algorithm is used to provide a policy for local control. Our results indicate that such a framework is capable of target-finding within a simulated indoor test environment in the presence of unknown obstacles, and once extended to real-world operation could enable UAVs to be applied in an increasing number of applications.
Multi-UAV Target-Finding in Simulated Indoor Environments using Deep Reinforcement Learning
01.03.2020
8224728 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
OVERTAKING MANEUVERS IN SIMULATED HIGHWAY DRIVING USING DEEP REINFORCEMENT LEARNING
British Library Conference Proceedings | 2018
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