Objective In recent years, the use of machine learning (ML) for the study of physics has experienced a strong boost. However, most of the machines used are black boxes, and the causal relation between inputs and outputs is often impossible to extract. Nonetheless, a critical aspect when dealing with physical systems is not only to make correct predictions, but to understand the physical laws which underlie these assessments. Recently, an increasing number of works aim at developing interpretable ML methods, from which such hidden laws can be extracted. However, their application to physics has been often limited to supervised and unsupervised learning approaches.The aim of this project is: 1) construct an interpretable reinforcement learning method; 2) extract hidden rules and features in timely and paramount problems in physics. The method combines three well-established concepts of ML: projective simulation, graph neural networks (GNN) and hidden variable disentanglement. PS provides interpretable RL agents that can be trained for a variety of tasks, from the construction of quantum experiments, via skill acquisition in robotics, to the modelling of honeybee colonies. By enhancing their learning power and interpretability with GNNs and variable disentanglement, we will extract the hidden features of the systems the RL agents have interacted with and ultimately, the physical laws governing them. In particular, we will tackle problems in the field of condensed matter, where particles diffuse either passively or actively, to reach a target state. Moreover, we will consider ensembles of RL agents, so as to analyze not only the physical properties of the systems, but also their interactions and communication dynamics in the quest of a common target.The originality of the proposal is directly related to: 1) the methods that will be developed; 2) the systems of study; 3) most importantly, the information we will access and discover with the interpretable RL agents. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learningunsupervised learningnatural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learningnatural sciencesbiological scienceszoologyentomologyapidologysocial scienceslawnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Keywords Machine Learning Autonomous scientific discovery Programme(s) HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme Topic(s) HORIZON-MSCA-2021-PF-01-01 - MSCA Postdoctoral Fellowships 2021 Call for proposal HORIZON-MSCA-2021-PF-01 See other projects for this call Funding Scheme HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships Coordinator UNIVERSITAET INNSBRUCK Net EU contribution € 183 600,96 Address Innrain 52 6020 Innsbruck Austria See on map Region Westösterreich Tirol Innsbruck Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window EU contribution No data
