Current value-based multi-agent reinforcement learning methods optimize individual Q
values to guide individuals' behaviours via centralized training with decentralized execution …

Uncertainty is prevalent in engineering design and data-driven problems and, more broadly,
in decision making. Due to inherent risk-averseness and ambiguity about assumptions, it is …

One major obstacle that precludes the success of reinforcement learning in real-world
applications is the lack of robustness, either to model uncertainties or external disturbances …

Abstract Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such
as finance. We derive concentration bounds for CVaR estimates, considering separately the …

Known finite-sample concentration bounds for the Wasserstein distance between the
empirical and true distribution of a random variable are used to derive a two-sided …

Classical multi-armed bandit problems use the expected value of an arm as a metric to
evaluate its goodness. However, the expected value is a risk-neutral metric. In many …

In this paper, we consider risk-sensitive sequential decision-making in Reinforcement
Learning (RL). Our contributions are two-fold. First, we introduce a novel and coherent …

In this article, we provide finite-sample performance guarantees for control policies executed
on stochastic robotic systems. Given an open-or closed-loop policy and a finite set of …

Abstract Conditional Value at Risk ($\textsc {CVaR} $) is a``coherent risk measure''which
generalizes expectation (reduced to a boundary parameter setting). Widely used in …

Green investments play a crucial role in fighting climate change and facilitating the shift
towards a low-carbon economy in line with goals of the Paris Agreement. This paper focuses …