… Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement
learning (RL… To facilitate and accelerate research and benchmarking in multi-objective RL prob…

… 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 reinforcement learning-based dynamic multi-objective evolutionary algorithm, called RL-DMOEA,
which seamlessly integrates reinforcement learning … CEC 2015 benchmark problems …

… The Pyramid MDP is a new and simple multi-objective benchmark, which we introduce in
this paper. A visual representation of the world is depicted in Figure 7 (a). The agent starts in …

… To this aim, we propose a benchmark Python framework that supports both single-policy
and multi-policy approaches to solving MODRL problems. Our framework is generic and highly …

… the multi-objective environments. We propose a novel multi-objective reinforcement learning
… , together with a few benchmark environments. We build further on this work and perform …

… benchmark the quality of our algorithms, we propose the first non-trivial high-dimensional
multi-objective … From raw visual input, an agent in Minecart must learn to adapt to the day’s …

… work on multi-objective optimization and reinforcement learning. … for multi-objective RL,
together with a few benchmark … of these benchmark environments using the multiobjective Q-…

… (3) At empirical level, we provide new evaluation metrics and benchmark environments
for MORL and apply our algorithm to a wider variety of domains including two complex larger …

… Extensive experiments have been conducted to study the DRL-MOA and various benchmark
… Murata, “A multi-objective genetic local search algorithm and its application to flowshop …