Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the
distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in …

Wasserstein Gradient Flows (WGF) with respect to specific functionals have been widely
used in the machine learning literature. Recently, neural networks have been adopted to …

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user
is derived from a single execution of a policy. In these settings, making decisions based on …

In offline reinforcement learning (RL), updating the value function with the discrete-time
Bellman Equation often encounters challenges due to the limited scope of available data …

In many real-world scenarios, the utility of a user is derived from a single execution of a
policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the …

Learning to predict rewards is a fundamental driver of adaptive behavior. Midbrain
dopamine neurons (DANs) play a key role in such learning by signaling reward prediction …

A critical part of achieving robust and safe navigation for mobile robots is selecting the right
navigation policies trained through simulation to operate effectively in real-world situations …

For effective decision support in scenarios with conflicting objectives, sets of potentially
optimal solutions can be presented to the decision maker. We explore both what policies …

Abstract Distributional Reinforcement Learning (RL) maintains the entire probability
distribution of the reward-to-go, ie the return, providing more learning signals that account …

Research in multi-objective reinforcement learning (MORL) has introduced the utility-based
paradigm, which makes use of both environmental rewards and a function that defines the …