论文标题
多样性参与者 - 批评：样品感知熵正规化，用于样品有效探索
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
论文作者
论文摘要
在本文中，提出了样本感知的政策熵正规化，以增强常规的政策熵正规化，以更好地探索。利用可从重放缓冲液获得的样本分布，提出的样品感知熵正则正规化可最大化策略行动分布的加权总和的熵，并从重播缓冲液中的样本操作分布进行样本效率探索。通过将策略迭代应用于目标函数，通过提出的样本感知熵正则化来开发一种名为多样性参与者批评的实用算法（DAC）。数值结果表明，DAC明显胜过现有的增强学习算法。
In this paper, sample-aware policy entropy regularization is proposed to enhance the conventional policy entropy regularization for better exploration. Exploiting the sample distribution obtainable from the replay buffer, the proposed sample-aware entropy regularization maximizes the entropy of the weighted sum of the policy action distribution and the sample action distribution from the replay buffer for sample-efficient exploration. A practical algorithm named diversity actor-critic (DAC) is developed by applying policy iteration to the objective function with the proposed sample-aware entropy regularization. Numerical results show that DAC significantly outperforms existing recent algorithms for reinforcement learning.
