The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …

Restricting the variance of a policy's return is a popular choice in risk-averse Reinforcement
Learning (RL) due to its clear mathematical definition and easy interpretability. Traditional …

In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model
from historically collected data, and subsequently utilize the learned model and fixed …

Modern navigation algorithms based on deep reinforcement learning (RL) show promising
efficiency and robustness. However, most deep RL algorithms operate in a risk-neutral …

Although distributional reinforcement learning (DRL) has been widely examined in the past
few years, there are two open questions people are still trying to address. One is how to …

Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and
personalization. Powered by modern AI technologies such as multimodal large language …

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