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artificial intelligence – Reinforcement learning and Graph Neural Networks: Issue with entropy

I am currently working on an experiment to link reinforcement learning with graph neural networks.
This is my architecture:

Feature Extraction with GCN:

  • there is a fully meshed topology with 23 nodes. Therefore there are 23*22=506 edges.
  • the original feature vector comprises 43 features that range from about -1 to 1.
  • First, a neuronal network f takes calculates a vector per edge, given the source node and target node features.
  • After we have calculated 506 edge vectors, function u aggregates the results from f per node (aggregation over 22 edges)
  • A function g takes the original target feature vector and concatenates the aggregated results from u. Finally, the output dimension of g determines the new feature vector size for each node.
  • At last, the function agg decides which information is returned from the feature extraction, e.g. just flatten the 23xg_output_dim feature vectors or building the average

After that:

  • The output of the feature extractor is passed to the OpenAi Baseline PPO2 Implementation. The frameworks adds a flatten layer to the output and maps it to 19 action values and 1 value-function value.

I have made some observations in the experiments and do not manage to explain them. Hyperparameter are: an output dimension for f and g of 512. U=sum, aggr=flatten. A tanh activation is applied on the outputs of f and g. For PPO2: lr=0.000343, stepsize=512.

This gives me the following weight matrices:

<tf.Variable 'ppo2_model/pi/f_w_0:0' shape=(86, 512) dtype=float32_ref>    
<tf.Variable 'ppo2_model/pi/g_w:0' shape=(555, 512) dtype=float32_ref>   
<tf.Variable 'ppo2_model/pi/w:0' shape=(11776, 19) dtype=float32_ref>    
<tf.Variable 'ppo2_model/vf/w:0' shape=(11776, 1) dtype=float32_ref>

The following problem occurs. Normally you wait for the entropy in the PPO2 to decrease during the training, because the algorithm learns which actions lead to more reward.
With the described hyperparameters, the entropy drops abruptly to 0 within 100 update steps and stays zero even after >15.000 updates (=150M steps in the game). This means that the same action is always selected.

What I found out: the problem is that by making the sum over 22 edges, very large values are created (maximum 221 and 22-1). The values are then given to the function g and thus ends up in the saturation region of the tanh. As a result, the new features of the 23 nodes contain many 1‘s and -1‘s. Because we flatten, the weighted sum of 11776 input neurons flows into each of the 19 action neurons, resulting in very large values in the policy. An action is then calculated from the policy with the following formula:

u = tf.random_uniform(tf.shape(logits), dtype=logits.dtype)
action = tf.argmax(logits - tf.log(-tf.log(u)), axis=-1), 

Most of the time tf.log(-tf.log(u) gives sommething between 2 and -2 (in my opinion). This means that as soon as a very large value appears in the policy, the corresponding action is always selected and not the second or third most probable one, which might lead to more exploration.

What I don’t understand 1): As soon as negative reward occurs, shouldn’t the likelihood decrease again, so that in the end I choose other actions again?

I did some experiments with relu and elu activations:
These are the value histogram of the output of g after the using relu, tanh and elu:
enter image description here
These are the value histograms of the policy, when using relu, tanh and elu:
enter image description here
Histogram over resulting actions:

What I don’t understand 2:Using Relu you see that in the first steps in the policy were large values, but then the model learns to reduce the range, which is why in this example also the entropy does not drop. Why does this not work when using tanh or elu?
enter image description here

We have found out 3 things with which the problem does not occur or is delayed. These are my assumptions. Are the correct in your opinion?:

  • using smaller output dimension of f and g, like 6 or using aggr=mean -> For each of the 19 action neurons, less input neurons are averaged -> smaller values in the policy –> more exploration
  • Using u=mean and not sum, averages the outputs of f, therefore the aggregated values are not only 1 and -1
  • Smaller learning rate -> Making the weights too big, increases the chance of the 19 action values to be big. If there is no negativ reward, there is no need for the algorithm to make the weights smaller.

I know this is a lot of information, so I would be grateful for any small tip.!

timestamp – Plausible way to alter time stamp on cctv recordings

I’m writing a screenplay (just a spec script – not for money (yet)). I need to write a scene realistically enough so it doesn’t seem stupid. A little jargon is useful. It may be impossible, but here’s what needs to happen.

Criminals need to edit out 47 seconds of cctv recording. The cctv footage is from a system (can be any system) that is in a small motel. They edit two sections – person walking in and later walking out of motel. The whole recording the police later seize is over 14 hours long. They need to edit out 47 seconds without it being completely obvious. There does need to be a flaw, but not easily discovered. The 14 hour tape looks fine to law enforcement – but it’s off. Any time stamp just needs to not match actual time.

The criminals would have had total access to the control room/motel system. They have total, unlimited access to admin privileges.

How could that plausibly be done?

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Market Research Online Shops

Hi everyone, I am currently starting something and want to ask 2 very simple questions:

  1. As an owner of an Online Shop, what are the 2 biggest issues you are dealing with in respect to your shop?
  2. As an owner of an Online Shop, what would you wish for more than anything else in respect to your shop?

If you also have a Mobile Shopping App for your Store, it would be awesome if you could answer the questions in respect to the App.

Thanks a lot in advance. I am looking…

Market Research Online Shops

❕NEWS – 10 years later managed to recover Bitcoin from burned disk | Proxies-free

That’s quite luck there considering she managed to retrieved her bitcoin from over 10 years, anyone or any other people would I think already give up after just 2 years or so.

A former hacker managed to recover her Bitcoins from her burned hard drive 10 years later. Twitter user @jonesushchrist, who describes himself as an ‘ex-hacker’, shared a screenshot showing that his wallet has accessed the wallet.dat file, saying, “I’m a millionaire!” used the expressions.
Do you think it’s pretty lucky?

I’m confused, Is the former hacker a man or woman? cause you used “her” in the first, then later you used “his”

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javascript – I need to remove the video in the background by pressing the button JS

const backVideo = document.querySelector("video");
var videoButton = document.getElementById("VideoOf")
var videoButtonOn = document.getElementById("VideoOn")

function videoOf() {
  backVideo.style.opacity = "0"
  if (backVideo.style.opacity = "0"){
    videoButton.style.opacity = "0"
    videoButtonOn.style.opacity = "1"
    
  } 
}
function videoOn() {
  backVideo.style.opacity = "1"
  if(backVideo.style.opacity = "1"){
    videoButtonOn != null;
    videoButton.style.opacity = "1"
    videoButtonOn.style.opacity = "0"
  }
}

sharepoint online – Repost Page not inheriting site column when added via Site Script

I have a Site Script where I add Site Columns to the Site Pages library. My issue is when adding site columns via Site Script the Repost Page content type is not inheriting the added columns, only the Site Page content type. Adding a column via the UI works as there is an option “Add to all content types” which is missing in site script. Sample of the approach:

{
  "$schema": "https://developer.microsoft.com/json-schemas/sp/site-design-script-actions.schema.json",
  "actions": (            
    {
      "verb": "createSPList",
      "listName": "Site Pages",
      "templateType": 119,
      "subactions": (
        {
          "verb": "addSiteColumn",
          "internalName": "PageCategory",
          "addToDefaultView": true
        },
        ...
      }
    )
  }

So, I do have a solution where I instead can update the base site content type (Repost Page) however I suspect this violates good practice on what to do and not to do in SharePoint. Or, is the below OK:

    {
  "$schema": "https://developer.microsoft.com/json-schemas/sp/site-design-script-actions.schema.json",
  "actions": (
    {
      "verb": "createContentType",
      "name": "Site Page",
      "description": "Create a new site page",
      "hidden": false,
      "parentName": "Document",
      "subactions":
        (
            {
              "verb": "addSiteColumn",
              "internalName": "PageCategory"
            }           
        )
    },
    {
      "verb": "createContentType",
      "name": "Repost Page",
      "description": "Used to create a News link post. If deleted, the News link option will be disabled for users.",
      "hidden": false,
      "parentName": "Site Page",
      "subactions":
        (
          {
            "verb": "addSiteColumn",
            "internalName": "PageCategory"
          }
        )
    },        
    {
      "verb": "createSPList",
      "listName": "Site Pages",
      "templateType": 119,
      "subactions": (
        {
          "verb": "addContentType",
          "name": "Site Page"          
        },
        {
          "verb": "addContentType",
          "name": "Repost Page"
        }
        ...
      )
   }
 }

I was hoping to be able to use Site Designs/Site Scripts for this and know that alternatives exists such as PnP Provisioning etc. My hope is also to avoid creating my own Content Type.

Any thoughts on the above? Is it OK to update the base Content Type?