5 papers
Analyzing Adversarial Inputs in Deep Reinforcement Learning
Davide Corsi, Guy Amir, Guy Katz +1
In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems. However,…
Benchmarking Interaction, Beyond Policy: a Reproducible Benchmark for Collaborative Instance Object Navigation
Edoardo Zorzi, Francesco Taioli, Yiming Wang +4
We propose Question-Asking Navigation (QAsk-Nav), the first reproducible benchmark for Collaborative Instance Object Navigation (CoIN) that enables an explicit, separate assessment…
Seldonian Reinforcement Learning for Ad Hoc Teamwork
Edoardo Zorzi, Alberto Castellini, Leonidas Bakopoulos +2
Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical appli…
Sentinel: Multi-Patch Transformer with Temporal and Channel Attention for Time Series Forecasting
Davide Villaboni, Alberto Castellini, Ivan Luciano Danesi +1
Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. Most of the state-of-the-art architecture…
Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues
Francesco Taioli, Edoardo Zorzi, Gianni Franchi +4
Language-driven instance object navigation assumes that human users initiate the task by providing a detailed description of the target instance to the embodied agent. While this d…