5 papers
Towards Tsallis Fully Probabilistic Design
Vyacheslav Kungurtsev, Giovanni Russo
Fully Probabilistic design (FPD) is a powerful framework offering an elegant and unifying account of stochastic control, learning and decision-making. Here we introduce a generaliz…
Free-Energy Minimizing Policies Under Generative Model Ambiguity
Arash Shafiei, Caio César Graciani Rodrigues, Giovanni Russo
We present a variational free-energy formulation for distributionally robust decision-making with ambiguity in the generative model. The formulation, related to a broad range of le…
Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
Hozefa Jesawada, Giovanni Russo, Abdalla Swikir +1
A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the…
Distributionally Robust Free Energy Principle for Decision-Making
Allahkaram Shafiei, Hozefa Jesawada, Karl Friston +1
Despite their groundbreaking performance, autonomous agents can misbehave when training and environmental conditions become inconsistent, with minor mismatches leading to undesirab…
DR-PETS: Learning-Based Control With Planning in Adversarial Environments
Hozefa Jesawada, Antonio Acernese, Giovanni Russo +1
Ensuring robustness against epistemic, possibly adversarial, perturbations is essential for reliable real-world decision-making. While the Probabilistic Ensembles with Trajectory S…