  In this paper, we consider multi-objective reinforcement learning, which
arises in many real-world problems with multiple optimization goals. We
approach the problem with a max-min framework focusing on fairness among the
multiple goals and develop a relevant theory and a practical model-free
algorithm under the max-min framework. The developed theory provides a
theoretical advance in multi-objective reinforcement learning, and the proposed
algorithm demonstrates a notable performance improvement over existing baseline
methods.
