Davide Tateo Team
Davide Tateo
Quick Info
Research Interests
Reinforcement Learning; Robotics; Deep Reinforcement Learning;
More Information
Google Scholar Curriculum Vitae
Contact Information
Mail. Davide Tateo
TU Darmstadt, Fachgebiet IAS
Hochschulstraße 10
64289 Darmstadt
Office.
Room E303,
Robert-Piloty-Gebaeude S2|02
+49-6151-16-20811
davide.tateo@tu-darmstadt.de
Davide Tateo is a postdoctoral researcher in the Intelligent Autonomous Systems group working on Robotics and Reinforcement Learning. Davide joined the lab in April 2019 after receiving his Ph.D. in Information Technology from Politecnico di Milano (Milan, Italy) in February 2019. He is currently working on the SKILLS4ROBOTS project, whose objective is to develop humanoid robots that can acquire and improve a rich set of motor skills.During his Ph.D. research, Davide worked under the supervision of prof. Andrea Bonarini and prof. Marcello Restelli focusing in particular on Hierarchical and Inverse Reinforcement Learning. During his Ph.D., he also co-developed MushroomRL, a Reinforcement Learning python library.
Software
- MushroomRL: A Python Reinforcement Learning Library, developed by Carlo D'Eramo and me, that provides both a clear interface to various benchmarking environments and simulators and implementation of many classical and deep reinforcement learning algorithms.
Key references
Parisi, S.; Tateo, D.; Hensel, M.; D'Eramo, C.; Peters, J.; Pajarinen, J. (submitted). Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning, Submitted to the Journal of Machine Learning Research (JMLR).
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Akrour, R.; Tateo, D.; Peters, J. (in press). Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
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Carvalho, J., Tateo, D., Muratore, F., Peters, J. (2021). An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients, International Joint Conference on Neural Networks (IJCNN).
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Deramo, C.; Tateo, D.; Bonarini, A.; Restelli, M.; Peters, J. (2021). MushroomRL: Simplifying Reinforcement Learning Research, Journal of Machine Learning Research (JMLR).
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Liu, P.; Tateo D.; Bou-Ammar, H.; Peters, J. (2021). Efficient and Reactive Planning for High Speed Robot Air Hockey, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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D`Eramo, C.; Tateo, D.; Bonarini, A.; Restelli, M.; Peters, J. (2020). Sharing Knowledge in Multi-Task Deep Reinforcement Learning, International Conference in Learning Representations (ICLR).
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Urain, J.; Ginesi, M.; Tateo, D.; Peters, J. (2020). ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows, IEEE/RSJ International Conference on Intelligent Robots and Systems.
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Urain, J.; Tateo, D.; Ren, T.; Peters, J. (2020). Structured policy representation: Imposing stability in arbitrarily conditioned dynamic systems, NeurIPS 2020, 3rd Robot Learning Workshop, pp.7.
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Tateo, D. (2019). Building structured hierarchical agents, Ph.D. Thesis.
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Beretta, C.; Brizzolari, C.; Tateo, D.; Riva, A.; Amigoni F. (2019). A Sampling-Based Algorithm for Planning Smooth Nonholonomic Paths, European Conference on Mobile Robots (ECMR).
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Tateo, D.; Erdenlig, I. S.; Bonarini, A. (2019). Graph-Based Design of Hierarchical Reinforcement Learning Agents, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.
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Akrour, R.; Tateo, D.; Peters, J. (2019). Towards Reinforcement Learning of Human Readable Policies, ECML/PKDD Workshop on Deep Continuous-Discrete Machine Learning.
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Tateo, D.; Banfi, J.; Riva, A.; Amigoni, F.; Bonarini, A. (2018). Multiagent Connected Path Planning: PSPACE-Completeness and How to Deal with It, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI2018), pp.4735-4742.
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Tateo, D.; D'Eramo, C.; Nuara, A.; Bonarini, A.; Restelli, M. (2017). Exploiting structure and uncertainty of Bellman updates in Markov decision processes, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
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Tateo, D.; Pirotta, M.; Restelli, M.; Bonarini, A. (2017). Gradient-based minimization for multi-expert Inverse Reinforcement Learning, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).
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