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A distributional view on multi-objective policy optimization

Abbas Abdolmaleki · Sandy Huang · Leonard Hasenclever · Michael Neunert · Francis Song · Martina Zambelli · Murilo Martins · Nicolas Heess · Raia Hadsell · Martin Riedmiller

Keywords: [ Deep Reinforcement Learning ] [ Robotics ] [ Reinforcement Learning - Deep RL ]


Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We propose to learn an action distribution for each objective, and we use supervised learning to fit a parametric policy to a combination of these distributions. We demonstrate the effectiveness of our approach on challenging high-dimensional real and simulated robotics tasks, and show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.

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