Multi-Objective Deep Reinforcement Learning

Hossam Mossalam, Yannis M. Assael, Diederik M. Roijers, Shimon Whiteson
10/9/2016
cs.AI

Official code: hossam-mossalam/multi-objective-deep-rl (45 stars, last commit 2017-01-01).

Abstract

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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Code Implementations(1)

hossam-mossalam/multi-objective-deep-rlOfficial100%

Multi-Objective Deep Reinforcement Learning

459LuaSep 16, 20169 years ago

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Cite this paper

@article{mossalam2016multiobjective,
  title  = {Multi-Objective Deep Reinforcement Learning},
  author = {Hossam Mossalam and Yannis M. Assael and Diederik M. Roijers and Shimon Whiteson},
  year   = {2016},
  eprint = {1610.02707},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1610.02707}
}

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