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).
Download Article BibTeX Reference
Akrour, R.; Tateo, D.; Peters, J. (submitted). Reinforcement Learning from a Mixture of Interpretable Experts, Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
Download Article BibTeX Reference
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).
Download Article BibTeX Reference
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).
Download Article BibTeX Reference
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.
Download Article BibTeX Reference
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.
Download Article BibTeX Reference
Tateo, D. (2019). Building structured hierarchical agents, Ph.D. Thesis.
Download Article BibTeX Reference
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).
Download Article BibTeX Reference
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.
Download Article BibTeX Reference
Akrour, R.; Tateo, D.; Peters, J. (2019). Towards Reinforcement Learning of Human Readable Policies, ECML/PKDD Workshop on Deep Continuous-Discrete Machine Learning.
Download Article BibTeX Reference
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.
Download Article BibTeX Reference
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).
Download Article BibTeX Reference
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).
Download Article BibTeX Reference
