… algorithms (MORL) extend standard reinforcement learning (… research and benchmarking
in multi-objective RL problems, … a benchmark suite for multi-objective reinforcement learning. …

… This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL)
framework based on deep Q-networks. We develop a high-performance MODRL framework …

… state of the art in MultiObjective Reinforcement Learning (MORL) benchmarking for problems
with … While this paper examined individual benchmarks, creating a larger benchmark suite, …

… variant of Q-learning [26, 39, 54], learning a manifold of … also learning- or regularization-focused
objectives such as staying close to a behavioral prior. Like MO-MPO, our multi-objective …

… 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 …

… In the experimental section, benchmark suites are used to test the performance of RLMMDE.
Different types of advanced algorithms are used to verify the performance of the proposed …

… Through a number of proofs, the authors show the original DST problem to be quite basic,
and not always representative of practical Multi-Objective Optimization (MOO) problems. In an …

… paper, a reinforcement learning based adaptive operator selection method has been proposed
for evolutionary multi-objective optimization. By using deep neural networks to learn the …

… , so we will first present a general discussion of multi-objective Q-learning before … As such,
rather than evaluating a wide range of algorithms across the complete benchmark suite, we …

… We propose a novel multi-objective reinforcement learning algorithm that successfully
learns the optimal policy even for non-linear utility functions. Non-linear utility functions pose a …