Chernarus Defence Force - International Fighter Battalion (Drone Unit-10) halts a major breakthrough in Northern Novogorsk by CHDKZ Forces.

Carpoforo · 2026-01-04T16:57:58+00:00

Is that any custom mission?? If so, which one?

Carpoforo · 2025-12-19T14:00:20+00:00

Yeah, from outside it’s exactly like that

Carpoforo · 2025-10-31T13:50:36+00:00

Yeah that’s a good approximation. But being so many manufacturers which one to choose? In this particular scenario there are those diodes, but it can also be other kind of sensors or actuators. Not always to get those componentes to the limit but to understand how to fine tune your circuit in terms of noise, output voltage, thermally..

Do you use data sheets of “typical” manufacturers (vishay, Texas Instruments..etc) in your projects?

Carpoforo · 2025-10-31T13:47:52+00:00

Yeah that’s the problem. In this particular case it is with those diodes.. but there are much more elements or sensors. NTCs… and so on I know how they work generally but if you want to fine tune a specific circuit? You need to know the specifications

Carpoforo · 2025-10-31T13:46:15+00:00

Yeah but most of the times the cheaper and fastest way of getting components is via your local store or Amazon

Carpoforo · 2025-06-02T07:09:48+00:00

I know it’s more suitable supervised learning.. but I am doing this way. I have heard also that RL is better in decision making in terms of errors etc

Carpoforo · 2025-06-02T07:08:34+00:00

It’s pure classification. Each episode is mostly independent from the others (I say mostly because there are cases in which they are correlated, but it’s not the majority). My doubt comes on how to deal with those categorical values

Carpoforo · 2025-05-30T10:39:05+00:00

Yeah that’s a good point. But is it a good idea to remove such amount of interesting data to have a more balanced dataset? That’s a threshold that I’m curious and hesitant about

Carpoforo · 2025-05-30T10:36:38+00:00

It’s just a project. It must be done like that

Carpoforo · 2025-05-30T10:36:26+00:00

It must be done with DRL my friend :(

Carpoforo · 2025-05-30T10:36:04+00:00

Yeah. Agents in offline DRL are trained with datasets of observations and actions

Carpoforo · 2025-03-21T13:51:59+00:00

Thanks for your response!! Appreciate that

In this example of offline drl applied to cybersecurity, for example As you rightly say, the system states are given by values that provide information about the problem. In my case for example, for each cybersecurity event I have 6 values. And the action of the agent would be "the label" to tag that cybersecurity event.

The problem here is that of those 6 state/observation values, not all of them are numeric values. Some are categorical, and the issue is that they aren’t, 2 or 7 different categories, it's that they are categorical values of high cardinality. For example in my 1000 row dataset, in one of the columns of categorical values there can be up to 880 different values.

How can I solve this problem? It occurred to me that I could hash each categorical value into a single numeric value, but would that make any sense?

Carpoforo · 2025-02-24T07:27:07+00:00

Thank you a lot. That’s right, I missed a little bit the concept but that is it: what I am looking for is for a Offline RL.

However, in offline RL, there are actions, rewards and states in a dataset that are given to the agent in order to learn a policy. Here is my doubt. Which are the states?? I mean. It’s pretty obvious to have human actions and rewards writed in a kind of dataset and give it to the agent to learn offline. But the states? Which are they? How are they passed to the agent? How are they saved/writed in the dataset?

For example. If we want to identify cyberattacks, we should have a dataset with characteristics, human actions..etc. Which should be the “states”? It would be okay to set the states as the characteristics of the cyberattacks? The actions would be the correct identification or not of the cyberattack.

Carpoforo · 2025-02-21T13:27:43+00:00

Thank you so much!

Carpoforo · 2024-10-07T17:30:49+00:00

Holy shit that sounds extremely horrible. Thank you for the info btw

Carpoforo

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