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
work+49-6151-16-20811

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 [PDF]   BibTeX Reference [BibTex]

Akrour, R.; Tateo, D.; Peters, J. (submitted). Reinforcement Learning from a Mixture of Interpretable Experts, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).   Download Article [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

Deramo, C.; Tateo, D.; Bonarini, A.; Restelli, M.; Peters, J. (2021). MushroomRL: Simplifying Reinforcement Learning Research, Journal of Machine Learning Research (JMLR).   BibTeX Reference [BibTex]

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).   Download Article [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

Tateo, D. (2019). Building structured hierarchical agents, Ph.D. Thesis.   Download Article [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

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 [PDF]   BibTeX Reference [BibTex]

  

zum Seitenanfang