Local utopia policy selection for multi objective reinforcement learning
Many real world applications are characterized by multiple conflicting objectives. In such problems, optimality is replaced by Pareto optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi objective optimization, the selection, given the Pareto frontier, of a Pareto optimal policy is still an important problem, prominent in practical applications such as economics and robotics. In this paper, we present a versatile approach for selecting a policy from the Pareto frontier according to user defined preferences. Exploiting a novel scalarization function and heuristics, our approach provides an easy to use and effective method for Pareto optimal policy selection. Furthermore, the scalarization is applicable in multiple policy learning strategies for approximating Pareto frontiers. To show the simplicity and effectiveness of our algorithm, we evaluate it on two problems and compare it to classical multi objective reinforcement learning approaches.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)