论文标题
软演员评论的加权熵修饰
Weighted Entropy Modification for Soft Actor-Critic
论文作者
论文摘要
我们通过用一些定性权重来表征国家行动对，将增强术学习（RL）中最大的香农熵（RL）的现有原理概括为加权熵，这些权重可以与策略的先验知识，经验重播和演变过程相连。我们提出了一种通过引入的重量功能进行自平衡探索的算法，尽管其实施方面简单，但仍导致在Mujoco任务上的最新性能。
We generalize the existing principle of the maximum Shannon entropy in reinforcement learning (RL) to weighted entropy by characterizing the state-action pairs with some qualitative weights, which can be connected with prior knowledge, experience replay, and evolution process of the policy. We propose an algorithm motivated for self-balancing exploration with the introduced weight function, which leads to state-of-the-art performance on Mujoco tasks despite its simplicity in implementation.
