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    A deep reinforcement learning based homeostatic system for unmanned position control

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    A deep reinforcement learning ...
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    Authors
    Manning, Warren
    Anjum, Ashiq
    Bower, Craig
    Dassanayake, Priyanthi
    Affiliation
    University of Derby
    Issue Date
    2019-12-05
    
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    Abstract
    Deep 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%.
    Citation
    Dassanayake, 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.
    Publisher
    Association for Computing Machinery
    URI
    http://hdl.handle.net/10545/624551
    Additional Links
    https://dl.acm.org/doi/proceedings/10.1145/3365109
    Type
    Meetings and Proceedings
    Language
    en
    ISBN
    9781450370165
    Collections
    Department of Mechanical Engineering & the Built Environment

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