| 查看: 883 | 回复: 5 | |||
| 【悬赏金币】回答本帖问题，作者agong将赠送您 5 个金币 | |||
| 当前只显示满足指定条件的回帖，点击这里查看本话题的所有回帖 | |||
[求助]
求助审稿意见的理解
|
|||
|
字面意思看懂了，但是还请过来人看看，然后发表一些批评和建议。以及之后该怎么样修改和下一步的投稿，谢谢大家。 发自小木虫IOS客户端 |
回复此楼» 猜你喜欢
复试调剂
已经有3人回复
-
中科大材料299求调剂
已经有16人回复
-
296求调剂
已经有3人回复
-
A区一本交叉课题组，低分调剂，招收机械电子信息通信等交叉方向
已经有65人回复
-
一志愿中科大材料与化工。353分能调剂到哪？
已经有3人回复
-
材料调剂
已经有7人回复
-
320材料与化工，求调剂
已经有10人回复
-
337求调剂
已经有3人回复
-
化工284求调剂
已经有7人回复
-
一志愿南大化学339分求调剂，四六级已过，有比赛，有文章
已经有4人回复
|
Review 2 Relevance and Timeliness Technical Content and Scientific Rigour Novelty and Originality Quality of Presentation Good. (4) Solid work of notable importance. (4) Some interesting ideas and results on a subject well investigated. (3) Well written. (4) Strong Aspects (Comments to the author: What are the strong aspects of the paper?) The paper proposes an improved experience based replay reinforcement learning algorithm (EBRL) for computation offloading by using MEC. The energy consumption and delay can be minimized by using the proposed algorithm compared with other algorithms. The paper is well written. Weak Aspects (Comments to the author: What are the weak aspects of the paper?) It is better to show more practical situation for performance comparison with considering realistic applications. Currently, only arrival rate is changed for considering different environment. Recommended Changes (Recommended changes. Please indicate any changes that should be made to the paper if accepted.) Please see the weak aspects. It is better to consider more realistic and practical situation. Robustness for environment change is another key performance for MEC offloading. |
5楼2021-12-15 18:23:15
2楼2021-12-15 17:44:11
3楼2021-12-15 18:15:41
|
Strong Aspects (Comments to the author: What are the strong aspects of the paper?) In this paper, the authors proposed an experience-based computational offloading with reinforcement learning in MEC network. Weak Aspects (Comments to the author: What are the weak aspects of the paper?) 1. In (11), it seems that the discount factor is 1, while the discount factor is defined as [0,1] in (12). It is not very clear. 2. Some symbols are undefined, i.e., the immediate reward r_t, the symbol \wedge in (15) 3. There are some flaw in the presentation, i.e., double “the task” in section II-B, the action should be defined in lowercase. 4. In algorithm 1, the meaning of “undated” is not clear. 5. It is better to compare the proposed algorithm with DQN not DDPG. Recommended Changes (Recommended changes. Please indicate any changes that should be made to the paper if accepted.) In this paper, the authors proposed an experience-based computational offloading with reinforcement learning in MEC network. The reviewer has the following comments. 1. In (11), it seems that the discount factor is 1, while the discount factor is defined as [0,1] in (12). It is not very clear. 2. Some symbols are undefined, i.e., the immediate reward r_t, the symbol \wedge in (15) 3. There are some flaw in the presentation, i.e., double “the task” in section II-B, the action should be defined in lowercase. 4. In algorithm 1, the meaning of “undated” is not clear. 5. It is better to compare the proposed algorithm with DQN not DDPG. |
4楼2021-12-15 18:22:55
5