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@robot-learning.de
tateo@ias.informatik.tu-darmstadt.de
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).
See Details Download Article BibTeX Reference
Deramo, C.; Tateo, D.; Bonarini, A.; Restelli, M. and Peters, J. (2020). Sharing Knowledge in Multi-Task Deep Reinforcement Learning, International Conference in Learning Representations.
See Details Download Article BibTeX Reference
Tateo, D. (2019). Building structured hierarchical agents, Ph.D. Thesis.
See Details 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.
See Details 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.
See Details 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.
See Details 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).
See Details 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).
See Details Download Article BibTeX Reference
