- 1RabbitMQ入门_14_Policies
- 2Docker安装ElasticSearch/ES 7.10.0_docker安装elasticsearch7.10
- 3【机器学习入门】(5) 决策树算法实战：sklearn实现决策树，实例应用（沉船幸存者预测）附python完整代码及数据集_深度学习 决策树实战
- 4[AI]算法小抄-总结大模型微调方式_算法微调
- 5cs231-11_特征向量转为图片
- 6【毕业设计】Java基于微信小程序的企业员工考勤系统_员工考勤小程序
- 7使用Office Tool Plus+KMS部署Office365和Visio等工具_office tool plus kms
- 8NO.6——Unity3D中两种绘制小地图的方法_使用unity3d绘制地图展示区域
- 9迁移到云原生：如何使用微服务迁移应用程序
- 10教程：AT32F435 QSPI 读写W25Q256
强化学习 reward 曲线的绘制_强化学习奖励曲线
赞
踩
每隔一段取一个均值，然后把均值曲线绘制出来，包含全部点的曲线淡化处理
摘自 Z. Mou, Y. Zhang, F. Gao, H. Wang, T. Zhang and Z. Han, “Deep Reinforcement Learning based Three-Dimensional Area Coverage with UAV Swarm,” in IEEE Journal on Selected Areas in Communications, doi: 10.1109/JSAC.2021.3088718.
说明文字:
Fig. 9 shows the rewards of SDQN, the variants of SDQN and other RL algorithms during the training process. The number of training episodes is set to be 800 with 200,000 steps each. Note that SDQN-nC represents the SDQN algorithm with no CNN in observation history model, and SDQN-nD is the SDQN algorithm with no panel divisions of terrain Q in advance. From Fig. 9, we can see that the rewards of SDQN rise much more quickly than that of the other four algorithms. The final rewards of SDQN-nC are less than that of SDQN, which indicates that the CNN in observation history model correctly extracts the features of coverage information of each LUAV and its neighbors. Moreover, the rewards of SDQN-nD rise slower than that of both SDQN and SDQN-nC, which indicates that the panel divisions based on prior knowledge play an important part in the performance improvement. From the high vibrating rewards curve of SDQN-nD, we can see that the panel divisions will reduce the performance variance of LUAVs by increasing the disciplines of patch selections for LUAVs. Furthermore, SDQN has better performance than both Actor Critic and REINFORCE algorithms. The rewards of Actor Critic have lower variance than the rewards of REINFORCE, because Actor Critic algorithm uses an extra critic network to guide the improvement directions of policies.
只绘制出了每一小段的均值，没有体现波动
摘自 Ding R, Xu Y, Gao F, et al. Trajectory Design and Access Control for Air-Ground Coordinated Communications System with Multi-Agent Deep Reinforcement Learning[J]. IEEE Internet of Things Journal, 2021.
摘自 Liu X, Liu Y, Chen Y, et al. Machine learning aided trajectory design and power control of multi-UAV[C]//2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019: 1-6.
