Multi-Objective Deep Reinforcement Learning

2016-10-09 · 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.

📄 PDF Abstract BibTeX arXiv:1610.02707

Code (2)

hossam-mossalam/multi-objective-deep-rl 공식 구현

lucasalegre/morl-baselines pytorch

Tasks

Deep Reinforcement LearningMulti-Objective Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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