Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …

Multiobjective reinforcement learning (MORL) extends RL to problems with multiple
conflicting objectives. This paper argues for designing MORL systems to produce a set of …

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

Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement
learning (RL) to scenarios where agents must optimize multiple---potentially conflicting …

Multi-objective reinforcement learning (MORL) is the generalization of standard
reinforcement learning (RL) approaches to solve sequential decision making problems that …

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear
preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is …

While a number of algorithms for multiobjective reinforcement learning have been proposed,
and a small number of applications developed, there has been very little rigorous empirical …

In multi-objective problems, it is key to find compromising solutions that balance different
objectives. The linear scalarization function is often utilized to translate the multi-objective …

This paper is about learning a continuous approximation of the Pareto frontier in Multi-
Objective Markov Decision Problems (MOMDPs). We propose a policy-based approach that …

Many real-world problems require trading off multiple competing objectives. However, these
objectives are often in different units and/or scales, which can make it challenging for …