Aiming for safe control, Inverse Constrained Reinforcement Learning (ICRL) considers
inferring the constraints respected by expert agents from their demonstrations and learning …

Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems
where agents are required to achieve goals by maximizing the expected return while …

Reinforcement learning algorithms utilizing policy gradients (PG) to optimize Conditional
Value at Risk (CVaR) face significant challenges with sample inefficiency, hindering their …

Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss
distributions have shown promise in developing hedging strategies in derivatives markets. A …

In Markov decision processes (MDPs), quantile risk measures such as Value-at-Risk are a
standard metric for modeling RL agents' preferences for certain outcomes. This paper …

Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years
due to the growing interest in deploying RL agents in real-world scenarios. A critical aspect …

Recent years have seen a rapid growth of reinforcement learning (RL) research. In year
2015, deep RL achieved superhuman performance in Atari video games (Mnih et al., 2015) …

What makes many decisions in sports difficult is that they involve a trade-off between risk
and reward. Actions such as taking a three-point shot, carrying a puck, or dribbling with a …

Abstrakt Viele Entscheidungen im Sport sind deshalb so schwierig, weil sie eine Abwägung
zwischen Risiko und Belohnung beinhalten. Aktionen wie ein Drei-Punkte-Schuss, das …