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dc.contributor.authorManning, Warren
dc.contributor.authorAnjum, Ashiq
dc.contributor.authorBower, Craig
dc.contributor.authorDassanayake, Priyanthi
dc.date.accessioned2020-03-05T15:35:51Z
dc.date.available2020-03-05T15:35:51Z
dc.date.issued2019-12-05
dc.identifier.citationDassanayake, P.M., Anjum, A., Manning, W. and Bower, C., (2019). 'A deep reinforcement learning based homeostatic system for unmanned position control'. Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Auckland, New Zealand, 2-5 December, pp. 127-136.en_US
dc.identifier.isbn9781450370165
dc.identifier.urihttp://hdl.handle.net/10545/624551
dc.description.abstractDeep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.urlhttps://dl.acm.org/doi/proceedings/10.1145/3365109en_US
dc.rights.urihttp://www.acm.org/publications/policies/copyright_policy#Background
dc.sourceProceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT '19
dc.subjectDeep Learningen_US
dc.subjectDeep Reinforcement Learningen_US
dc.subjectArtificial Immune Systemen_US
dc.titleA deep reinforcement learning based homeostatic system for unmanned position controlen_US
dc.typeMeetings and Proceedingsen_US
dc.contributor.departmentUniversity of Derbyen_US
dcterms.dateAccepted2019-10-16
refterms.dateFOA2020-03-26T09:44:13Z
dc.author.detail785921en_US


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