论文标题
探索变分深Q网络
Exploring Variational Deep Q Networks
论文作者
论文摘要
这项研究提供了分析，也提供了唐和库库克尔比尔（Tang and Kucukelbir）变分深Q网络的精致，研究就绪的实施，这是一种新型的方法，是使用变分贝叶斯推论在复杂学习环境中最大化探索效率的一种新方法。除了传统和双重Q网络的参考实现之外，还提出了一个小新颖的贡献 - 双变异深Q网络，该网络结合了改进，以提高基于推理的学习的稳定性和鲁棒性。最后，在贝叶斯深度学习的更广泛背景下讨论了这些方法有效性的评估和讨论。
This study provides both analysis and a refined, research-ready implementation of Tang and Kucukelbir's Variational Deep Q Network, a novel approach to maximising the efficiency of exploration in complex learning environments using Variational Bayesian Inference. Alongside reference implementations of both Traditional and Double Deep Q Networks, a small novel contribution is presented - the Double Variational Deep Q Network, which incorporates improvements to increase the stability and robustness of inference-based learning. Finally, an evaluation and discussion of the effectiveness of these approaches is discussed in the wider context of Bayesian Deep Learning.
