3 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
FoSeRL: Formal Sequential Robustness Certification for Reinforcement Learning Policies
Sara Taheri, Deep Kumar Ganguly, Jan Křetínský +1
Even a few action perturbations can substantially degrade the performance of a deployed decision policy. Certifying the resulting return loss is challenging in stochastic environme…
Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk
Deep Kumar Ganguly, Jan Křetínský
An agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this through a robust-optimization identity--…
Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making
Deep Kumar Ganguly, Jan Kretinsky
How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties caution to epistemic uncertainty:…
dtControl2+: Trading Optimality for Explainability in MDPs via Decision Trees
Tereza Kinská, Jan Křetínský, Tobias Meggendorfer +2
Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, f…
Explainably Safe Reinforcement Learning
Sabine Rieder, Stefan Pranger, Debraj Chakraborty +2
Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whos…