In reinforcement learning (RL), the consideration of multivariate reward signals has led to
fundamental advancements in multi-objective decision-making, transfer learning, and …

Multi-objective reinforcement learning (MORL) has been proposed to learn control policies
over multiple competing objectives with each possible preference over returns. However …

Multi-agent systems are characterized by environmental uncertainty, varying policies of
agents, and partial observability, which result in significant risks. In the context of Multi-Agent …

We consider the problem of learning models for risk-sensitive reinforcement learning. We
theoretically demonstrate that proper value equivalence, a method of learning models which …

Reinforcement learning (RL) has been widely adopted for controlling and optimizing
complex engineering systems such as next-generation wireless networks. An important …

Due to the nature of risk management in learning applicable policies, risk-sensitive
reinforcement learning (RSRL) has been realized as an important direction. RSRL is usually …

We study risk-sensitive reinforcement learning (RL), a crucial field due to its ability to
enhance decision-making in scenarios where it is essential to manage uncertainty and …

In domains such as finance, healthcare, and robotics, managing worst-case scenarios is
critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement …

A major challenge of reinforcement learning (RL) in real-world applications is the variation
between environments, tasks or clients. Meta-RL (MRL) addresses this issue by learning a …

In the realm of reinforcement learning (RL), accounting for risk is crucial for making
decisions under uncertainty, particularly in applications where safety and reliability are …