Emergent Solutions to High-Dimensional Multitask Reinforcement Learning

@article{Kelly2018EmergentST,
  title={Emergent Solutions to High-Dimensional Multitask Reinforcement Learning},
  author={Stephen R Kelly and Malcolm I. Heywood},
  journal={Evolutionary Computation},
  year={2018},
  volume={26},
  pages={347-380}
}
Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning (RL), increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is being paid to frameworks from deep learning, which scale to high-dimensional data by decomposing the task through multilayered neural networks. While effective, the representation is complex and computationally demanding. In this work, we propose a… CONTINUE READING
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