  Multi-objective reinforcement learning (MORL) is the generalization of
standard reinforcement learning (RL) approaches to solve sequential decision
making problems that consist of several, possibly conflicting, objectives.
Generally, in such formulations, there is no single optimal policy which
optimizes all the objectives simultaneously, and instead, a number of policies
has to be found each optimizing a preference of the objectives. In other words,
the MORL is framed as a meta-learning problem, with the task distribution given
by a distribution over the preferences. We demonstrate that such a formulation
results in a better approximation of the Pareto optimal solutions in terms of
both the optimality and the computational efficiency. We evaluated our method
on obtaining Pareto optimal policies using a number of continuous control
problems with high degrees of freedom.
