… 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 …

… action selection purposes in on-line reinforcement learning. Instead, we propose a novel
non-… previous work on multi-objective optimization and reinforcement learning. Furthermore, we …

… multi-objective optimization problems. In our work, we focus … reinforcement learning for
multi-objective problems. Reinforcement learning (Sutton and Barto, 1998) is a machine learning …

… 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 …

… We argue that multi-objective methods are underrepresented … justify the need for explicitly
multi-objective approaches. Key to … of multi-objective RL, ie, that the appropriate 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 …

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

… Abstract—This paper describes a novel multi-objective reinforcement learning algorithm. …
-based reinforcement learning algorithm for solving multi-objective reinforcement learning …

… 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 …

… 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 …