  Multi-objective Markov decision processes are a special kind of
multi-objective optimization problem that involves sequential decision making
while satisfying the Markov property of stochastic processes. Multi-objective
reinforcement learning methods address this problem by fusing the reinforcement
learning paradigm with multi-objective optimization techniques. One major
drawback of these methods is the lack of adaptability to non-stationary
dynamics in the environment. This is because they adopt optimization procedures
that assume stationarity to evolve a coverage set of policies that can solve
the problem. This paper introduces a developmental optimization approach that
can evolve the policy coverage set while exploring the preference space over
the defined objectives in an online manner. We propose a novel multi-objective
reinforcement learning algorithm that can robustly evolve a convex coverage set
of policies in an online manner in non-stationary environments. We compare the
proposed algorithm with two state-of-the-art multi-objective reinforcement
learning algorithms in stationary and non-stationary environments. Results
showed that the proposed algorithm significantly outperforms the existing
algorithms in non-stationary environments while achieving comparable results in
stationary environments.
