Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures

机译：利用MMC架构利用域名知识进行强化学习

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Despite the success of reinforcement learning methods in various simulated robotic applications, end-to-end training suffers from extensive training times due to high sample complexity and does not scale well to realistic systems. In this work, we speed up reinforcement learning by incorporating domain knowledge into policy learning. We revisit an architecture based on the mean of multiple computations (MMC) principle known from computational biology and adapt it to solve a "reacher task". We approximate the policy using a simple MMC network, experimentally compare this idea to end-to-end deep learning architectures, and show that our approach reduces the number of interactions required to approximate a suitable policy by a factor of ten.

机译：尽管在各种模拟机器人应用中取得了加强学习方法，但由于高样本复杂性而导致的最终培训遭受了广泛的培训时间，并且对现实系统不符。在这项工作中，我们通过将域名知识纳入政策学习来加速强化学习。我们根据计算生物学中已知的多个计算（MMC）原则的平均值来重新筛选架构，并使其适应“到达任务”。我们使用简单的MMC网络估计策略，通过实验将这个想法与端到端的深度学习架构进行比较，并表明我们的方法减少了近似适当政策所需的交互数量。

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《International Conference on Artificial Neural Networks》|2019年|xxx 807 p.|共13页
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Rajkumar Ramamurthy; Christian Bauckhage; Rafet Sifa; Jannis Schuecker; Stefan Wrobel;
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4. Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures [C] . Rajkumar Ramamurthy, Christian Bauckhage, Rafet Sifa, International Conference on Artificial Neural Networks . 2019

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8. KI-LEARN: Knowledge-Intensive Learning Methods for Knowledge-Rich/Data- Poor Domains [R] . Dietterich, T. G. , Restificar, A. , Tadepalli, P. , 2006

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Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures

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