We had two papers accepted at AI&Stats’16 on multi-armed bandit exploiting the arm structure to reduce the regret in reward maximization and the probability of error in best-arm identification. Online …
We presented two novel results on apprenticeship learning at IJCAI’15, where we use demonstrations generated by an expert to learn near-optimal policies. This scenario is typical in source-to-target transfer, where the …
We have presented novel results on how to exploit the structure of the arm set in multi-armed bandit. In particular, we consider the case where by pulling one action additional …
The paper on “Sparse Multi-Task Reinforcement Learning” in collaboration with D. Calandriello and M. Restelli (PoliMi) has been accepted for publication at NIPS’14. Abstract. In multi-task reinforcement learning (MTRL), the …