Papers › Multi-Objective Deep Reinforcement Learning

Multi-Objective Deep Reinforcement Learning

9 Oct 2016arXiv:1610.02707archive 2025-07-28

Hossam Mossalam, Yannis M. Assael, Diederik M. Roijers, Shimon Whiteson

We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of the objectives. To our knowledge, this is the first time that deep reinforcement learning has succeeded in learning multi-objective policies. In addition, we provide a testbed with two experiments to be used as a benchmark for deep multi-objective reinforcement learning.

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Deep Reinforcement Learning Multi-Objective Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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