Publications

Ashley W.D. Hill, Jean Laneurit, Roland Lenain, Eric Lucet (2022). Online Gain Tuning Using Neural Networks: A Comparative Study. AgriEngineering 2022.

Ashley W.D. Hill, Jean Laneurit, Roland Lenain, Eric Lucet (2022). Online Tuning of Control Parameters for Off-Road Mobile Robots: Novel Deterministic and Neural Network-Based Approaches. RAM 2022.

Ashley W.D. Hill, Eric Lucet, Roland Lenain (2022). A Novel Gradient Feature Importance Method for Neural Networks: An Application to Controller Gain Tuning for Mobile Robots. LNEE, volume 793.

François Gauthier-Clerc, Ashley W.D. Hill, Jean Laneurit, Roland Lenain, Eric Lucet (2021). Online velocity fluctuation of off-road wheeled mobile robots: A reinforcement learning approach. ICRA 2021.

Antonin Raffin, Ashley W.D. Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann (2021). Stable-baselines3: Reliable reinforcement learning implementations. JMLR.

Ashley W.D. Hill, Jean Laneurit, Roland Lenain, Eric Lucet (2020). Online gain setting method for path tracking using CMA-ES: Application to off-road mobile robot control. IROS 2020.

Ashley W.D. Hill, Eric Lucet, Roland Lenain (2020). A new neural network feature importance method: Application to mobile robots controllers gain tuning. ICINCO 2020.

Ashley W.D. Hill, Eric Lucet, Roland Lenain (2019). Neuroevolution with CMA-ES for real-time gain tuning of a car-like robot controller. ICINCO 2019.

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Antonin Raffin, Ashley W.D. Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat (2019). Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics.

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Antonin Raffin, Ashley W.D. Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat (2018). S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning. In Deep RL Workshop NIPS.

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