Timeline for How can we program Reinforcement learning without transition probability and rewards?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Jan 8, 2020 at 13:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 7, 2019 at 15:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 30, 2018 at 23:49 | comment | added | Rachel | Yes, exactly correct what you described! In that assumption, can you give some ideas to my questions? | |
| Sep 29, 2018 at 4:04 | comment | added | Karel Macek | I am little bit lost in what is the very essence of your problem. My understanding is that the state of your environment can be described as a vector that expresses the proportion of types of tasks in different distribution centers. For example, if we have 2 distribution centers and 3 types of tasks, you don't have detailed data, but just $s_{i,j}$ where $i=1,2$ and $j=1,2,3$ and $s_{i,j}$ is the number of tasks at distribution center $i$ and task $j$. Is my understanding correct? | |
| Sep 28, 2018 at 23:18 | answer | added | Lucas Roberts | timeline score: 1 | |
| Sep 28, 2018 at 19:37 | answer | added | numerairX | timeline score: 2 | |
| Sep 28, 2018 at 19:05 | review | First posts | |||
| Sep 29, 2018 at 4:04 | |||||
| Sep 28, 2018 at 19:02 | history | asked | Rachel | CC BY-SA 4.0 |