标题： VASE：强化学习中的变分多样惊喜探索 显示英文标题

标题： VASE: Variational Assorted Surprise Exploration for Reinforcement Learning

摘要： 在具有连续控制和稀疏奖励的环境中进行探索仍然是强化学习（RL）中的一个关键挑战。 最近，意外被用作一种内在奖励，以鼓励系统且高效的探索。 我们引入了一个新的意外定义及其名为变分混合意外探索（VASE）的RL实现。 VASE使用贝叶斯神经网络作为环境动态的模型，并使用变分推断进行训练，交替更新代理模型的准确性和策略。 我们的实验表明，在连续控制稀疏奖励环境中，VASE优于其他基于意外的探索技术。 显示英文摘要

摘要： Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration. We introduce a new definition of surprise and its RL implementation named Variational Assorted Surprise Exploration (VASE). VASE uses a Bayesian neural network as a model of the environment dynamics and is trained using variational inference, alternately updating the accuracy of the agent's model and policy. Our experiments show that in continuous control sparse reward environments VASE outperforms other surprise-based exploration techniques.