Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them.
Multi-task learning is inherently a multi-objective problem because different tasks may …

Many real-world control problems involve conflicting objectives where we desire a dense
and high-quality set of control policies that are optimal for different objective preferences …

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 …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

We present methods for co-designing rigid robots over control and morphology (including
discrete topology) over multiple objectives. Previous work has addressed problems in single …

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

In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is
an expensive process. The set of optimal policies can grow exponentially with the number of …

Many real-world control applications, from economics to robotics, are characterized by the
presence of multiple conflicting objectives. In these problems, the standard concept of …

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 …