A Distributional View on Multi-Objective Policy Optimization

• URL: http://arxiv.org/abs/2005.07513v1
• Date: Fri, 15 May 2020 13:02:17 GMT
• Title: A Distributional View on Multi-Objective Policy Optimization
• Authors: Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin Riedmiller
• Abstract summary: We propose an algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.
• Score: 24.690800846837273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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