… This paper serves as a guide to the application of multi-objective methods to difficult …
single-objective reinforcement learning and planning methods who wish to adopt a multi-objective …

… a new scalable multi-objective deep reinforcement learning (… of different deep reinforcement
learning algorithms in different … with standard multi-objective reinforcement learning methods …

… As a baseline, we use a basic Multi-Objective DQN approach (MO); a single multi-objective
DQN continuously trained on only the current w through scalarized Deep Q-learning. MO …

… In this paper, a reinforcement learning-based dynamic multi-objective evolutionary algorithm,
called RL-DMOEA, which seamlessly integrates reinforcement learning framework and …

… 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 to learn policies over …

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

… Our key insight is to view constrained RL from a multi-objective perspective… multi-objective
RL framework. We first formulate the constrained RL problem as a Constrained Multi-Objective …

… using reinforcement learning strategies based on a novel prediction-guided evolutionary
learning … In order to benchmark our proposed algorithm, we design a set of multi-objective robot …

… for solving multiobjective optimization problems (MOPs) using deep reinforcement learning
(DRL… Murata, “A multi-objective genetic local search algorithm and its application to flowshop …

… To the best of our knowledge, we apply Multi-Objective Reinforcement Learning (MORL)
in the setting of RS for the first time and explore some of the many possibilities and future …