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Understanding biological plume tracking behavior using deep reinforcement-learning.

, , , and . ALIFE, page 750-752. MIT Press, (2020)

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History dependence in insect flight decisions during odor tracking., , , , and . PLoS Comput. Biol., (2018)PyNumDiff: A Python package for numerical differentiation of noisy time-series data., , , and . J. Open Source Softw., 7 (71): 4078 (2022)Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes., , , and . CoRR, (2021)A Passively Stable Hovering Flapping Micro-Air Vehicle., , and . Flying Insects and Robots, Springer, (2010)FigureFirst: A Layout-first Approach for Scientific Figures., , and . SciPy, page 57-63. scipy.org, (2017)Understanding biological plume tracking behavior using deep reinforcement-learning., , , and . ALIFE, page 750-752. MIT Press, (2020)Emergent behaviour and neural dynamics in artificial agents tracking odour plumes., , , and . Nat. Mac. Intell., 5 (1): 58-70 (January 2023)Empirical Individual State Observability., , and . CDC, page 8450-8456. IEEE, (2023)A Nonlinear Observability Analysis of Ambient Wind Estimation with Uncalibrated Sensors, Inspired by Insect Neural Encoding.. CDC, page 1399-1406. IEEE, (2021)Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework., , and . IEEE Access, (2020)