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      "permlink": "understand-the-deep-q-learning-method-an-usefull-technic-for-ai",
      "title": "Understand The deep Q learning method : an usefull technic for AI",
      "body": "# Understand The deep Q learning method : an usefull technic for AI\n\n## Foreword\n\nOne of the most popular models in the deep reinforcement-learning sub-branch is the algorithm called Deep Q Network or DQN. Understanding this method is extremely important. It is the basis of many deep reinforcement-learning algorithms. The DQN model was first proposed by researchers at Google DeepMind in 2013 in a paper called Playing Atari with Deep Reinforcement Learning. In it, they described the DQN architecture and explained why this method was so effective especially for Atari games.\n\nIn this period, it is no exaggeration to say that the DQN method appeared in a good time. Indeed, other methods based on the Q function had made it possible to advance in the field of reinforcement learning. However, researchers remained at an impasse. At the same time, the tabular Q-learning method was on the rise and had solved some problems regarding the accumulation of states in reinforcement problems. Despite everything, it was not very effective in the context of observation of large ensembles. Therefore, from there to evoking a solution for the resolution of video games, it was inconceivable. The complexity was too great for them. Having to read all of the pixels and their states was inconceivable. The algorithms would have had too many states to consider. Tests have shown that the available reinforcement methods did not even come close to a satisfactory solution.\n\nEspecially since in some environments the number of observations is almost endless. Quickly, it was necessary to decide on the ranges of parameters to be taken into account to distinguish the crucial states from those that are not relevant. For a video game, a single pixel will not make a difference in a game. It is therefore possible to create a larger assembly as part of the image as a state. However, even so, it is necessary to be able to distinguish areas of the screen where the action is relevant to the sequence of events. One solution in this case has been to provide both a state and an action mapped into a single value. In machine learning, we talk about a regression problem. There are several concrete ways to represent and train this type of representation.\n\nHere we will describe one of them. Because of its efficiency and its ease in spreading over different problems, the Deep Q Network has become essential for reinforcement learning.\n\n## The genesis of DQN\n\nRemember that the goal of reinforcement learning as a whole is to find the optimal policy that will give us the maximum return. In order to calculate this policy, we first start by calculating the emblematic function of the DQN method, the Q function.\n\nRecall that the function Q is the function of value and state. It indicates the value of a state-action pair. Its use allows us to use the result obtained by the agent starting from a state s and executing the action “a” following a policy π.\n\nIn the case of the DQN method, we have to calculate the set of state-action values ​​with the Q function. However, in some cases it can be extremely long and complicated to calculate the Q-value of each action-state pair. Instead of calculating Q values ​​iteratively, we can use one of the many approximation functions. Here in the context of the DQN, it will be with a neural network that we will do. In this way, we can then parameterize our function Q with a parameter theta and calculate the Q value with parameter theta. We just feed the state of our environment into a neural network and it will return the Q value of all possible actions from that state. Once the Q values ​​are found, we can select the best action like the one with the maximum Q value. Since we are using a neural network to approximate the Q values, the neural network is called the Q network. Since most of the time we are dealing with a deep neural network to approximate the Q value, then our deep neural network is called a deep Q network or DQN. Now you know all about the genesis of DQN. However, at this point, many questions remain unanswered. Let us try to take it one step further.\n\n## The interactions with the environment\n\nBefore going into details about the DQN method, it is important to review the agent's interactions with its environment. We know that for the agent it is necessary to interact with the environment in order to receive rewards but also data for training. We can act randomly as is done in some cases. But in the context of a video game, for example, is it really relevant? What is the probability of winning in this scenario? It's very rare. So it will take several games before you achieve enough success. An alternative would be to use our approximate Q function as a source of behavior. We find the principle mentioned in the context of the iterative value method.\n\nIf our representation of Q is good, then the experience we get from acting on the environment will show us that the agent's data is valid for training. In a low-quality approximation, we detect it by experience. The concern here is that our agent can get caught up in bad deeds without trying to behave differently. We return to our dilemma of exploitation - exploration. It is important to give the agent the opportunity to explore the environment and build a series of transitions and actions with various outputs. Even though we should not let it act in just any manner, we are above all looking for efficiency. For example, it would be silly to reproduce actions whose sequence has already been presented and did not yield any interesting data.\n\nResearch has shown that an initially randomized system, when the approximation cannot be used, is very often an interesting alternative since it provides a basis that offers sufficient and varied interactions with the environment. Eventually, as our training system progresses, the randomized behavior becomes ineffective and we can model our approximation Q and choose what action to take. This one proposing with our training, a more serious approximation.\nA method that applies this proposition very well is the ϵ-greedy method. It will simply alternate between a random phenomenon and the policies of Q by using the hyper parameter ϵ. By varying ϵ, we choose the part of randomness in our actions. We start in practice with ϵ = 1.0, to gradually end around a value varying between 0.05 or 0.02 depending on the case. This method helps both to explore the environment at the start of the experiment, but also to maintain a good policy afterwards by switching to the Q function. There are other solutions to deal with the subject of exploration and exploitation. However, that will not be our subject here.\n\n## How to train a network in deep Q Learning ?\n\nThis is a question we can legitimately ask ourselves. Truthfully speaking, the good training of the network provides the quality of the results of the algorithm. So let us see the researchers' proposal on this subject.\n\nRecall that we run our algorithm with the network parameter θ. Its initial value is random in order to be able to start approaching the optimal Q function. Since we are in the phase of initializing the function, the result will never be optimal. We are therefore going to train the network over several iterations. We try by this principle to find the optimal parameter θ. Once the optimal parameter θ has been found, we will have the optimal Q function. Then we can extract the optimal policy from the optimal Q function.\n\nOne of the most popular solutions to find optimal θ and Q is the use of a deep neural network. We find this approach especially in the context of representation associated with images on the screen.\n\n### The training data:\n\nThe basis of Q-learning is borrowed from the supervised machine learning model. Indeed, we are trying to work with a complex nonlinear function, in our case Q with a neural network. To do this we have to calculate targets for the function using the Bellman equation to claim to have a supervised learning problem at hand. This is a good idea, but to use an optimization SGD (the little name behind this technique), our training data must be independent and identically distributed (I.I.D principle).\n\nIn our case, our data does not agree with this principle. To remedy this, we can use a buffer in which we record our past experiences and our training samples. This collection will replace the latest experiments. We are talking about the replay buffer technique. We use this buffer called replay buffer to collect the agent's experience. The replay buffer will simply serve as a collection of experiences that we set aside to use for training. From there, based on that experience, we train our network.\n\n### Replay buffer :\n\nThis tuple in our replay buffer, also called in French, experience buffer. We usually denote it with D. This information transition is what is trivially called the agent experience. The idea of using a buffer to store user experience is interesting since we train our DQN with the experience we sampled in the buffer.\n\nIn this way, we collect the agent's transition information during several episodes and we save it in the replay buffer. Transition information is stored in a stack (our replay buffer) where the newly entered data will be added to the bottom of the stack. However, this method has a disadvantage. In fact, we are going to store the experiences here one by one. From experience to experience, these will often be similar. To avoid this, we can integrate some random transition into the replay buffer before training the network.\n\nRemember that the replay buffer has a storage limit, and can therefore only keep a certain number of agent experiences. When this is filled, the new entries overwrite the old ones. The replay buffer is generally constructed like the structure of a queue. That is, so that the first entries are the first exits. Therefore, each addition after the full battery will eliminate the oldest experience and make room for the new one. At the same time, this allows the sample replay buffer to be improved. The further we go in training, the better the experiences tend to be.\n\n### Using the loss function, a great ally:\n\nIn our DQN method, we want to predict the values of Q. Now, these are continuous values. This will add complexity to the prediction. To overcome this, we use in DQN a regression task. The most popular method is the mean squared error, also noted MSE. It will come to play the role of loss function during the regression. The principle of the MSE can be translated as the squared average of the difference between the target value and the predicted value, we note that as follows:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.39.59.png)\n\nWhere y plays the role of target value, y \" of the predicted value and K the number of training samples.\n\nOur goal is to train our network and try to minimize the MSE between the target Q value and the predicted Q value as much as possible. Obviously, the goal is to get the optimal Q value, which also comes down to minimizing error. Since the difference between the target Q value and the current Q value tends towards 0 as one approaches the optimal Q value. We use the Bellman equation for this. You will find an article covering the concept of this equation here. We know that the optimal Q value can be obtained using the Bellman Optimal equation:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.49.16.png)\n\nWhere R (s, a, s ’) represents the immediate reward r, obtained by performing an action in state s leading to state s’. It is therefore possible to replace R (s, a, s ’) simply by r.\n\nIn our equation, we can also remove the expected factor E. Indeed, we will approximate this factor by taking a sample of K transitions from the replay buffer and recovering an average value. Thus according to the optimal Bellman equation, the optimal value Q is the sum of the rewards to which we add the maximum value Q reduced to the next action-state pair:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.52.04.png)\n\nWe can also define our loss as the difference between the target value (the optimal Q value) and the predicted value (the Q value predicted by DQN). The loss function will then be expressed:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.55.13.png)\n\nBy substituting the above equation for the previous one, we get this simplified formula:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.55.53.png)\n\nWe calculated the predicted Q-value using the network parameter θ. Now let's see how to calculate the target value. We have seen that it is the sum of the rewards to which we add the maximum Q value reduced to the next action-state pair.\n\nSimilar to the predicted Q-value, we can calculate the Q-value of the next action-state pair by using the same network parameter, θ. Indeed, notice in our equation that we have, the two values Q parameterized by θ.\n\nInstead of calculating the loss as just the difference between the target Q value and the predicted Q value, we use the MSE method as the loss function. Remember that we have our experiences stored in the replay buffer. In addition, we have a small part of our randomized sample. We then train the network and try to minimize the MSE. We can then translate our loss function as follows:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.56.38.png)\n\nWe have seen that the target value is simply the sum of the rewards and the maximum Q value reduced to the next action-state pair. Consider the case where state s is the terminal state. If s is the last step in an episode, then we cannot calculate the Q value, since we have no action to take in the terminal state. In this case, the target value will become our reward. Therefore, our loss function will look like:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.57.19.png)\n\nWe have trained our network by minimizing the loss function. However, we have seen that it is also possible to minimize the loss function by finding the optimal parameter θ. To do this, we can use the gradient descent method and find the optimal parameter θ (how can we reduce the MSE with the gradient descent?).\n\n\n### Correlation between stages:\n\nAnother problem with our Q-learning model concerns its training procedure. By default, it is based on a method that does not respect the i.i.d principle. The Bellman equation gives us values of Q (s, a) in direct relation to Q (s ’, a’) (we speak of bootstrapping). The concern being that between s and s, only one step has passed. This makes s and s very similar, too similar in fact. This makes their differentiation very difficult for the neural networks. So when we optimize our neural network parameters, to achieve a Q (s, a) value close to the desired result, we can quickly alter the value of Q (s ’, a’) with each iteration. This will make our training very unstable. We have an error propagation which can completely destroy our approximation of Q (s, a).\n\nThere is a trick to solve this problem. We can build a new network playing the role of the target. With this, we create a copy of our network that we will use for Q (s ’, a’) in the Bellman equation. This network is synchronized with the main network. We regularly and periodically synchronize the two networks. At every N step with N that will become one of DQN's hyperparameters, often between 1k and 10k.\n\n### The target network to the rescue\n\nDespite the steps already taken, we continue to have a little problem with our loss function. We know that the target value is the sum of the rewards plus the reduced maximum Q value at the next state-action pair. We calculate this Q value on the target and we predict the Q value using the same parameter θ. Now we have a problem since the target and predicted value both depend on the same parameter, in this case θ. This can cause instabilities when using the MSE and the networks will learn in a bad manner. It also causes a lot of discrepancy in training.\n\nHow can we avoid this? It is possible to freeze the target value for a time and calculate only the predicted value. To do this, we introduce another neural network called target network to calculate the Q value of the next state-action pair. The parameter of the target network is denoted θ '. Therefore, our main Q network will calculate the predicted Q values and learn the optimal θ parameter using the gradient descent method. The target network will be put on standby for a while in order to, after a period, resume operation and update the parameter θ ' by simply copying the parameter θ from the main network. We start to freeze the target network and so on. In this case, our loss function becomes:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.59.32.png)\n\nThe Q value of the next action state is calculated by the target network with the parameter θ 'and the predicted Q value is calculated by the main network with the parameter θ. If we go back to our simplified notation, we get:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-15.00.53.png)\n\n## The Markov property applied to Deep Q Learning\n\nOur DQ-learning method uses the formalism of the Markovian decision process. Which in principle specifies that the model obeys the Markov properties: According to this, the observations of the environment are all distributed in an optimal way. That is, each observation allows us to distinguish the entirety of the states. By analyzing a single image from a video game, we cannot retrieve all of the information. For example, the speed or direction of an object will not be known. This invalidates the Markov properties. Therefore, our system, in the case of video games, is relegated to the rank of Partial Markovian or MDPS or POMDPS decision process. We speak of POMDPS when we have a model with a Markovian decision process that does not respect the Markov properties. This method appears very regularly in a real context, such as in card games where the opponent's hand is unknown. There is a way to turn a POMDPS into an MDP.\n\n## Results\n\nAfter introducing so much new information and technical mathematical concepts, let us go back to the key elements for the DQN model.\n\nFirst, we update the general lattice parameter θ with a random value. We have learned that the target network is a simple copy of the general network. Then we update the parameter θ ' by copying the parameter θ. We also update D, our replay buffer.\n\nNow for each step in an episode we add the state of the environment in our network, and we get its outputs corresponding to the Q values for all possible actions in the state. Then we choose the action, which has the maximum Q value.\n\nIf we only select the stock with the highest Q value, then we don't explore other new stocks. To avoid this, we select our stocks by adding the ϵ-greedy policy. We select our random action with an epsilon probability and with the probability ϵ-1, we select the best action with the maximum Q value.\n\nSince we update our lattice parameter θ with a random value, the action we select by taking the maximum Q value will not necessarily be the optimal action. But that's okay, we're just improving the selected action. We then go to the next state and get the reward. If the action is good then we will receive a positive reward, otherwise, it will be negative. We store this information in replay buffer D.\n\nThen, in a random manner, we create a sample of K of transitions coming from the replay buffer for which we have previously calculated the loss parameter. Our loss function is given by:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-15.07.19.png)\n\nDuring the first iterations, the loss will be very high since our parameter θ of the network is configured randomly. To minimize the loss, we calculate the gradients of the losses and we improve our network parameter θ by following the principle of the gradient descent.\n\nWe do not change the target network parameter θ' at each step. We freeze the parameter θ' for several steps and then we inject the value of θ into θ'. We repeat this process for several episodes to approximate the optimal Q value. Once the optimal Q value is found, we extract the optimal policy.\n\nThe DQN process is certainly a process requiring an assimilation of all the steps that make up the method, it remains one of the most popular and effective reinforcement learning techniques. You will find it regularly in different variations during your research.\n\nLet see more : https://01codex.com/",
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      "author": "maxime01codex",
      "permlink": "improve-cybersecurity-structure-with-framework-nist-1-1",
      "title": "Improve Cybersecurity structure with Framework NIST 1.1",
      "body": "![NIST 1.1 Cybersecurity Framework](http://01codex.com/wp-content/uploads/2021/06/NIST.jpeg)\n\n\nTo be able to implement the various actions necessary for the security of a company, the National Institute of Standards and Technology (NIST) has developed a framework dedicated entirely to cybersecurity. Since this is an open-source and independent tool, it can serve as a basis for any organization wishing to validate its cybersecurity needs.\n\n## Genesis of the NIST project\n\nSince 1901, the NIST lab has worked regularly with the government of the United States of America to analyze and propose solutions in the area of industrial competitiveness. This was created to counterbalance the great advance of Germany and the United Kingdom at the beginning of the 20th century in the fields of economy and industry.\n\nWith its great expertise in the field of physical and economic measurement, during the emergence of new technologies, NIST has been the ideal candidate to address issues related to computer security.\n\nThe lab's cybersecurity program is based on the institution's fundamentals and promotes innovation, research and the study of US competitiveness.\n\nThe subject of data and the relationship with the outside world is a crucial subject for the government of the United States. As a result, NIST's research focuses on topics such as cyberattacks, new technologies, defense methodologies and data preservation. The laboratory is very often called upon for the creation of standards and to establish rules of defense for the industry. Collaborations with the main industrial players in the country are also the responsibility of the laboratory, in order to train strategic sectors on the subject of cybersecurity.\n\n\n## A place in the conspiracy\n\nWith such a broad area of expertise, the US Federal Laboratory very often assists the US government on sensitive issues. Following the attack on the two World Trade Center towers, the laboratory was approached to analyze the possible causes of the collapse of the Twin Towers.\n\nUsing a computer simulation, bringing together an enormous amount of data, the laboratory's responsibility was to determine the multi-factorial cause. The collapse is believed to be partly due to the fragility of the load-bearing columns, following the damage caused by the impact.\n\nIn any case, this is what the computer simulation revealed after analyzing the scenarios. After this short anecdote, let's come back to the subject of the NIST framework and its importance in the analysis of a defense strategy.\n\n## How does the NIST framework work?\n\n![NIST 1.1 Cybersecurity Framework](http://01codex.com/wp-content/uploads/2021/06/CSF.png)\n\nThe NIST 1.1 Framework is made up of five so-called fundamental functions for online security. These functions are:\n\n* Identify\n* Protect\n* Detect\n* Reply\n* Restructure\n\nThese five pillars will encompass the main processes to be put in place to secure an organization using digital technologies. Each of these points is divided into sub-categories, themselves grouping together tasks to be performed.\n\nThere are also specific sub-categories offering methods of integrating solutions to common situations. Obviously each part is accompanied by reference documents and case studies.\n\nThe Framework also offers an implementation hierarchy with levels providing companies with a way to situate their skills and actions in relation to the NIST standard.\n\nFinally, the profile section will offer an overview, bringing a long-term approach. The projection on a wider horizon, favors the application of the new habits to have within a company.\n\n## An interesting database\n\nTo go further, the laboratory's website offers a fairly large database dealing with the different areas around cybersecurity. This database, dating back more than 20 years, has the advantage of bearing witness to technological advances and the associated risks since the beginning of the 21st century. It’s a real gold mine for any cybersecurity enthusiast.\n\nThe topics offered are broad and encompass:\n\n* Encryption\n* Access control\n* Risk management\n* Artificial intelligence\n* Blockchains\n* Hardware\n* Servers\n* …\n\n## Why use the NIST Framework?\n\nThere are different reasons to look into the NIST Framework 1.1. Its use will allow among other things:\n\n* Understanding the risks associated with new technologies\n* Prevent and prepare for potential threats\n* Raise awareness of all stakeholders in an organization on the safety aspect\n* Validate the security of the tools put in place by a company or an individual\n\nThe platform offers free of charge the knowledge and tools necessary to implement an effective strategy against computer threats.\n\n## Reminder\n\nThe purpose of the NIST 1.1 framework is only to present a list of recommendations and to prevent risks associated with computer technologies. Compliance with the advice provided by the institution is the responsibility of the company via a critical self-assessment of its structure. Therefore, its use should be indicative and guide a process of raising awareness and improving cybersecurity.\n\nsee more : https://01codex.com",
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      "author": "maxime01codex",
      "permlink": "will-we-experience-a-computer-virus-pandemic",
      "title": "Will we experience a computer virus pandemic?",
      "body": "![Covid19 and cybersecurity](http://01codex.com/wp-content/uploads/2021/06/cdc-w9KEokhajKw-unsplash.jpg)\n\nWith the COVID-19 crisis, digital uses have experienced a dizzying increase. Its growth has, so to speak, increase exponentially. With the rise in connections to web services and the need to be able to access the Internet to weather the shock, we have seen an increase in computer viruses and cyber attacks. Whether on the corporate or personal side, everyone was a potential target for hackers during this time of crisis. With no curfew for cyber attacks, the government website cybermalveillance.gouv.fr noted a 400% increase in phishing attempts during the first week of containment.\n\nWith hindsight, even if the COVID period is still relevant, it is interesting to do prospective work on the similarities between COVID crisis management and cybersecurity risks. Here is a list of different reasons why it seems that the Covid-19 crisis can teach us more about the dangers posed by our large-scale digital use.\n\n## 1- The covid-19 pandemic, a golden opportunity for cyber attacks\n\nThe newspaper of the net questioned the presence of a \"digital pandemic\" in a publication in 2020 in parallel with the health crisis. The surge in online stocks spawned by the lockdown has been a boon for many malicious agents. First and foremost, the most frequent phishing attacks. They have the advantage of not needing any technical skills. A great threat for Internet users whose untargeted attack is made possible by many spamming software.\n\nAlthough phishing attacks are commonplace, they were most noticeable during containment. At the same time, more present but also more visible for the ultra-connected Internet users on the networks. This is where the term digital pandemic comes into its own. Indeed, like the scourge that is Covid-19, phishing attempts have proliferated on the internet, harming several victims already in bad shape by the health situation.\n\nThe exponential increase in cyber attacks is reminiscent of the increase in Covid19 cases that took place during the year 2020. Reminding us, everyone, of the need to use digital hygiene on the web.\n\n## 2-  A multifactorial cause difficult to detect\n\nFor a long time, the cause of Covid-19 has been a source of debate and controversy. Each bringing their theory to the now public sphere of digital media. Looking back, it would seem that the origin, but also the spread of the virus is actually caused by a positive feedback from many co-occurring factors.\n\nThe same is true when an individual sees their accounts and hardware infected with a computer virus. It is often difficult to return to the source of the evil, so scrupulous are the pirates that remove the leads. Also, digital security decreased in such a measure that users will leave flaws to appear during navigation. It is therefore difficult to know what is the source of the problem when it occurs.\n\n## 3- Solutions to reduce the spread and risks\n\nWith the increase in malware, spyware and whatnot, resorting to what might be called \"barrier gestures\" is the best solution. They allow us to prevent any penetration or theft of personal data. The irremediable advice on hygiene during the Covid-19 crisis is reminiscent of online safety rules, which we keep repeating all day long.\n\nIt is without context the accumulation of healthy and preventive practices that are the main defenses of individuals in the face of digital crime. For once, these tedious little practices, but effective in reducing risks, have always been presented by professionals, to best help Internet users. However, we can see it even more in the digital world, it is sometimes difficult to force a hand when it comes to potential risks.\n\n## 4- a threat not always taken seriously\n\nIndeed, during the health crisis, we had the right to many debacles about the gravity of the situation and the dangerousness of the threat. Every day, a protest emerged on various social networks, advocating a rather low mortality curve or mild symptoms. Everyone has their own opinion on how to deal with the crisis. What we are seeing, however, is that the perceived seriousness of a threat diminishes so much when it is not tangible and diffused.\n\nUnfortunately, what better place than the virtual digital world and the great discretion of malicious hackers, to create an atmosphere of calm before the storm. This is a very interesting point to raise, the denial of danger is still very much online. Internet users do not feel affected by the main threats that surround them. Yet it is by a factor of 4 that the number of cyber attacks increased during the year 2020, which raises questions about the future.\n\n## 5- A generational division on the stakes\n\nThe implementation of barrier gestures and the feeling of gravity linked to covid-19 was a source of discord between the different age groups. If fear has invaded our seniors, a legitimate response since they are the main ones affected by severe cases of the disease, young people have taken the recommendations a little less seriously. At least, this is a finding that many facts have highlighted.\n\nIn an equivocal way, the online presence and the respect of the rules concerning cybersecurity, have more or less impact according to the generations. The carelessness and somewhat candid appearance of young people shows a lack of consideration about online safety. On the senior side, it is the shortcomings in learning digital tools that are the cause of some deficiencies in online security measures. There are various reasons why the advice for preserving its digital security is not followed. Social pressure, habituation to lost freedoms, misunderstanding and many other factors have gradually established bad habits.\n\nIn an equivocal way, the online presence and the respect of the rules concerning cybersecurity, have more or less impact according to the generations. The carelessness and somewhat candid appearance of young people shows a lack of consideration about online safety. On the senior side, it is the shortcomings in learning digital tools that are the cause of some deficiencies in online security measures. There are various reasons why the advice for preserving its digital security is not followed. Social pressure, habituation to lost of freedoms, misunderstanding and many other factors have gradually established bad habits.\n\nJust like during the Covid-19 crisis, mistakes and non-compliance with the basic rules are the result of various factors. The generational aspect brings various reasons, which makes awareness raising more difficult for the vulgarizers.\n\n## 6 - Asymptomatic user accounts\n\nIf we look back at the feeling linked to the contamination, some Covid-19 patients did not have any noticeable symptoms during their infectious period. This did not make crisis management easier, as even in good faith, some individuals were able to spread the strain of the virus to others.\n\nWe find this situation identical with the escalating levels during a targeted assault where access is given little by little without us knowing that it has been compromised. Some digitally infected individuals are not even aware that their tools and software are infected. Subsequently, they will unwittingly transmit the contamination. Faced with this possibility, which is not without precedent, it is all the more important to be aware of this possibility and to maintain rigor when using it on the internet.\n\n## 7- The basic emotion of online attacks\n\nThe scams that took place during the Covid-19 crisis are countless and have particularly targeted vulnerable people. Despite the abject nature of attacking the most sensitive people, it is a well-known fact that scams and attacks are mainly aimed at them.\n\nEmotion plays a fundamental role in many online extortion strategies. Whether it's phishing, which is based only on the stress of lies, or ransomware strategies. The more the pirate captures the emotions of his target, the closer his success. Even if the education and popularization work on the subject has reduced panic phenomena following blackmailing online, this method of emotional hacking is still very present among hackers. Taking the time to educate yourself and rationalize the situation is the only response to such attacks.\n\n## 8- A question of individual freedom\n\nFinally, we cannot end without talking about the freedom and security dilemma. The Covid-19 crisis, especially among critics of measures taken by government institutions, has often highlighted a true philosophical subject, that of freedom. To what extent must my individual freedom be withdrawn from the common goods? This is a rhetorical question that comes up in any situation where security is required. As a result, cybersecurity is just as impacted.\n\nIn fact, to a lesser extent, the measures advocated by the defenders of a secure internet, block out the original vision of a libertarian internet without constraints. Do I really have to put aside certain freedoms, such as posting certain content on social networks, for the sole reason that they could possibly compromise me in the future? This choice is still up to everyone, even if in many cases it can be like playing with fire.\n\nFinally, looking at these eight examples, we see that there are many similarities between the health crisis and digital issues, which are tending to accelerate. This topic should be put on the front of the table for many experts. It makes it possible to carry out prospecting work on the basis of our experience in 2020. The benefits that could be brought to the fight against cybercrime are still diffuse. However, the management of communication and the integration of barrier gestures have been, overall, a success from which we can try to follow our example. What do you think ?",
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      "title": "Will we experience a computer virus pandemic?",
      "body": "![Covid19 and cybersecurity](http://01codex.com/wp-content/uploads/2021/06/cdc-w9KEokhajKw-unsplash.jpg)\n\nWith the COVID-19 crisis, digital uses have experienced a dizzying increase. Its growth has, so to speak, increase exponentially. With the rise in connections to web services and the need to be able to access the Internet to weather the shock, we have seen an increase in computer viruses and cyber attacks. Whether on the corporate or personal side, everyone was a potential target for hackers during this time of crisis. With no curfew for cyber attacks, the government website cybermalveillance.gouv.fr noted a 400% increase in phishing attempts during the first week of containment.\n\nWith hindsight, even if the COVID period is still relevant, it is interesting to do prospective work on the similarities between COVID crisis management and cybersecurity risks. Here is a list of different reasons why it seems that the Covid-19 crisis can teach us more about the dangers posed by our large-scale digital use.\n\n## 1- The covid-19 pandemic, a golden opportunity for cyber attacks\n\nThe newspaper of the net questioned the presence of a \"digital pandemic\" in a publication in 2020 in parallel with the health crisis. The surge in online stocks spawned by the lockdown has been a boon for many malicious agents. First and foremost, the most frequent phishing attacks. They have the advantage of not needing any technical skills. A great threat for Internet users whose untargeted attack is made possible by many spamming software.\n\nAlthough phishing attacks are commonplace, they were most noticeable during containment. At the same time, more present but also more visible for the ultra-connected Internet users on the networks. This is where the term digital pandemic comes into its own. Indeed, like the scourge that is Covid-19, phishing attempts have proliferated on the internet, harming several victims already in bad shape by the health situation.\n\nThe exponential increase in cyber attacks is reminiscent of the increase in Covid19 cases that took place during the year 2020. Reminding us, everyone, of the need to use digital hygiene on the web.\n\n## 2-  A multifactorial cause difficult to detect\n\nFor a long time, the cause of Covid-19 has been a source of debate and controversy. Each bringing their theory to the now public sphere of digital media. Looking back, it would seem that the origin, but also the spread of the virus is actually caused by a positive feedback from many co-occurring factors.\n\nThe same is true when an individual sees their accounts and hardware infected with a computer virus. It is often difficult to return to the source of the evil, so scrupulous are the pirates that remove the leads. Also, digital security decreased in such a measure that users will leave flaws to appear during navigation. It is therefore difficult to know what is the source of the problem when it occurs.\n\n## 3- Solutions to reduce the spread and risks\n\nWith the increase in malware, spyware and whatnot, resorting to what might be called \"barrier gestures\" is the best solution. They allow us to prevent any penetration or theft of personal data. The irremediable advice on hygiene during the Covid-19 crisis is reminiscent of online safety rules, which we keep repeating all day long.\n\nIt is without context the accumulation of healthy and preventive practices that are the main defenses of individuals in the face of digital crime. For once, these tedious little practices, but effective in reducing risks, have always been presented by professionals, to best help Internet users. However, we can see it even more in the digital world, it is sometimes difficult to force a hand when it comes to potential risks.\n\n## 4- a threat not always taken seriously\n\nIndeed, during the health crisis, we had the right to many debacles about the gravity of the situation and the dangerousness of the threat. Every day, a protest emerged on various social networks, advocating a rather low mortality curve or mild symptoms. Everyone has their own opinion on how to deal with the crisis. What we are seeing, however, is that the perceived seriousness of a threat diminishes so much when it is not tangible and diffused.\n\nUnfortunately, what better place than the virtual digital world and the great discretion of malicious hackers, to create an atmosphere of calm before the storm. This is a very interesting point to raise, the denial of danger is still very much online. Internet users do not feel affected by the main threats that surround them. Yet it is by a factor of 4 that the number of cyber attacks increased during the year 2020, which raises questions about the future.\n\n## 5- A generational division on the stakes\n\nThe implementation of barrier gestures and the feeling of gravity linked to covid-19 was a source of discord between the different age groups. If fear has invaded our seniors, a legitimate response since they are the main ones affected by severe cases of the disease, young people have taken the recommendations a little less seriously. At least, this is a finding that many facts have highlighted.\n\nIn an equivocal way, the online presence and the respect of the rules concerning cybersecurity, have more or less impact according to the generations. The carelessness and somewhat candid appearance of young people shows a lack of consideration about online safety. On the senior side, it is the shortcomings in learning digital tools that are the cause of some deficiencies in online security measures. There are various reasons why the advice for preserving its digital security is not followed. Social pressure, habituation to lost freedoms, misunderstanding and many other factors have gradually established bad habits.\n\nIn an equivocal way, the online presence and the respect of the rules concerning cybersecurity, have more or less impact according to the generations. The carelessness and somewhat candid appearance of young people shows a lack of consideration about online safety. On the senior side, it is the shortcomings in learning digital tools that are the cause of some deficiencies in online security measures. There are various reasons why the advice for preserving its digital security is not followed. Social pressure, habituation to lost of freedoms, misunderstanding and many other factors have gradually established bad habits.\n\nJust like during the Covid-19 crisis, mistakes and non-compliance with the basic rules are the result of various factors. The generational aspect brings various reasons, which makes awareness raising more difficult for the vulgarizers.\n\n## 6 - Asymptomatic user accounts\n\nIf we look back at the feeling linked to the contamination, some Covid-19 patients did not have any noticeable symptoms during their infectious period. This did not make crisis management easier, as even in good faith, some individuals were able to spread the strain of the virus to others.\n\nWe find this situation identical with the escalating levels during a targeted assault where access is given little by little without us knowing that it has been compromised. Some digitally infected individuals are not even aware that their tools and software are infected. Subsequently, they will unwittingly transmit the contamination. Faced with this possibility, which is not without precedent, it is all the more important to be aware of this possibility and to maintain rigor when using it on the internet.\n\n## 7- The basic emotion of online attacks\n\nThe scams that took place during the Covid-19 crisis are countless and have particularly targeted vulnerable people. Despite the abject nature of attacking the most sensitive people, it is a well-known fact that scams and attacks are mainly aimed at them.\n\nEmotion plays a fundamental role in many online extortion strategies. Whether it's phishing, which is based only on the stress of lies, or ransomware strategies. The more the pirate captures the emotions of his target, the closer his success. Even if the education and popularization work on the subject has reduced panic phenomena following blackmailing online, this method of emotional hacking is still very present among hackers. Taking the time to educate yourself and rationalize the situation is the only response to such attacks.\n\n## 8- A question of individual freedom\n\nFinally, we cannot end without talking about the freedom and security dilemma. The Covid-19 crisis, especially among critics of measures taken by government institutions, has often highlighted a true philosophical subject, that of freedom. To what extent must my individual freedom be withdrawn from the common goods? This is a rhetorical question that comes up in any situation where security is required. As a result, cybersecurity is just as impacted.\n\nIn fact, to a lesser extent, the measures advocated by the defenders of a secure internet, block out the original vision of a libertarian internet without constraints. Do I really have to put aside certain freedoms, such as posting certain content on social networks, for the sole reason that they could possibly compromise me in the future? This choice is still up to everyone, even if in many cases it can be like playing with fire.\n\nFinally, looking at these eight examples, we see that there are many similarities between the health crisis and digital issues, which are tending to accelerate. This topic should be put on the front of the table for many experts. It makes it possible to carry out prospecting work on the basis of our experience in 2020. The benefits that could be brought to the fight against cybercrime are still diffuse. However, the management of communication and the integration of barrier gestures have been, overall, a success from which we can try to follow our example. What do you think ?",
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      "title": "Will we experience a computer virus pandemic?",
      "body": "![Covid19 and cybersecurity](http://01codex.com/wp-content/uploads/2021/06/cdc-w9KEokhajKw-unsplash.jpg)\n\nWith the COVID-19 crisis, digital uses have experienced a dizzying increase. Its growth has, so to speak, increase exponentially. With the rise in connections to web services and the need to be able to access the Internet to weather the shock, we have seen an increase in computer viruses and cyber attacks. Whether on the corporate or personal side, everyone was a potential target for hackers during this time of crisis. With no curfew for cyber attacks, the government website cybermalveillance.gouv.fr noted a 400% increase in phishing attempts during the first week of containment.\n\nWith hindsight, even if the COVID period is still relevant, it is interesting to do prospective work on the similarities between COVID crisis management and cybersecurity risks. Here is a list of different reasons why it seems that the Covid-19 crisis can teach us more about the dangers posed by our large-scale digital use.\n\n## 1- The covid-19 pandemic, a golden opportunity for cyber attacks\n\nThe newspaper of the net questioned the presence of a \"digital pandemic\" in a publication in 2020 in parallel with the health crisis. The surge in online stocks spawned by the lockdown has been a boon for many malicious agents. First and foremost, the most frequent phishing attacks. They have the advantage of not needing any technical skills. A great threat for Internet users whose untargeted attack is made possible by many spamming software.\n\nAlthough phishing attacks are commonplace, they were most noticeable during containment. At the same time, more present but also more visible for the ultra-connected Internet users on the networks. This is where the term digital pandemic comes into its own. Indeed, like the scourge that is Covid-19, phishing attempts have proliferated on the internet, harming several victims already in bad shape by the health situation.\n\nThe exponential increase in cyber attacks is reminiscent of the increase in Covid19 cases that took place during the year 2020. Reminding us, everyone, of the need to use digital hygiene on the web.\n\n## 2-  A multifactorial cause difficult to detect\n\nFor a long time, the cause of Covid-19 has been a source of debate and controversy. Each bringing their theory to the now public sphere of digital media. Looking back, it would seem that the origin, but also the spread of the virus is actually caused by a positive feedback from many co-occurring factors.\n\nThe same is true when an individual sees their accounts and hardware infected with a computer virus. It is often difficult to return to the source of the evil, so scrupulous are the pirates that remove the leads. Also, digital security decreased in such a measure that users will leave flaws to appear during navigation. It is therefore difficult to know what is the source of the problem when it occurs.\n\n## 3- Solutions to reduce the spread and risks\n\nWith the increase in malware, spyware and whatnot, resorting to what might be called \"barrier gestures\" is the best solution. They allow us to prevent any penetration or theft of personal data. The irremediable advice on hygiene during the Covid-19 crisis is reminiscent of online safety rules, which we keep repeating all day long.\n\nIt is without context the accumulation of healthy and preventive practices that are the main defenses of individuals in the face of digital crime. For once, these tedious little practices, but effective in reducing risks, have always been presented by professionals, to best help Internet users. However, we can see it even more in the digital world, it is sometimes difficult to force a hand when it comes to potential risks.\n\n## 4- a threat not always taken seriously\n\nIndeed, during the health crisis, we had the right to many debacles about the gravity of the situation and the dangerousness of the threat. Every day, a protest emerged on various social networks, advocating a rather low mortality curve or mild symptoms. Everyone has their own opinion on how to deal with the crisis. What we are seeing, however, is that the perceived seriousness of a threat diminishes so much when it is not tangible and diffused.\n\nUnfortunately, what better place than the virtual digital world and the great discretion of malicious hackers, to create an atmosphere of calm before the storm. This is a very interesting point to raise, the denial of danger is still very much online. Internet users do not feel affected by the main threats that surround them. Yet it is by a factor of 4 that the number of cyber attacks increased during the year 2020, which raises questions about the future.\n\n## 5- A generational division on the stakes\n\nThe implementation of barrier gestures and the feeling of gravity linked to covid-19 was a source of discord between the different age groups. If fear has invaded our seniors, a legitimate response since they are the main ones affected by severe cases of the disease, young people have taken the recommendations a little less seriously. At least, this is a finding that many facts have highlighted.\n\nIn an equivocal way, the online presence and the respect of the rules concerning cybersecurity, have more or less impact according to the generations. The carelessness and somewhat candid appearance of young people shows a lack of consideration about online safety. On the senior side, it is the shortcomings in learning digital tools that are the cause of some deficiencies in online security measures. There are various reasons why the advice for preserving its digital security is not followed. Social pressure, habituation to lost freedoms, misunderstanding and many other factors have gradually established bad habits.\n\nIn an equivocal way, the online presence and the respect of the rules concerning cybersecurity, have more or less impact according to the generations. The carelessness and somewhat candid appearance of young people shows a lack of consideration about online safety. On the senior side, it is the shortcomings in learning digital tools that are the cause of some deficiencies in online security measures. There are various reasons why the advice for preserving its digital security is not followed. Social pressure, habituation to lost of freedoms, misunderstanding and many other factors have gradually established bad habits.\n\nJust like during the Covid-19 crisis, mistakes and non-compliance with the basic rules are the result of various factors. The generational aspect brings various reasons, which makes awareness raising more difficult for the vulgarizers.\n\n## 6 - Asymptomatic user accounts\n\nIf we look back at the feeling linked to the contamination, some Covid-19 patients did not have any noticeable symptoms during their infectious period. This did not make crisis management easier, as even in good faith, some individuals were able to spread the strain of the virus to others.\n\nWe find this situation identical with the escalating levels during a targeted assault where access is given little by little without us knowing that it has been compromised. Some digitally infected individuals are not even aware that their tools and software are infected. Subsequently, they will unwittingly transmit the contamination. Faced with this possibility, which is not without precedent, it is all the more important to be aware of this possibility and to maintain rigor when using it on the internet.\n\n## 7- The basic emotion of online attacks\n\nThe scams that took place during the Covid-19 crisis are countless and have particularly targeted vulnerable people. Despite the abject nature of attacking the most sensitive people, it is a well-known fact that scams and attacks are mainly aimed at them.\n\nEmotion plays a fundamental role in many online extortion strategies. Whether it's phishing, which is based only on the stress of lies, or ransomware strategies. The more the pirate captures the emotions of his target, the closer his success. Even if the education and popularization work on the subject has reduced panic phenomena following blackmailing online, this method of emotional hacking is still very present among hackers. Taking the time to educate yourself and rationalize the situation is the only response to such attacks.\n\n## 8- A question of individual freedom\n\nFinally, we cannot end without talking about the freedom and security dilemma. The Covid-19 crisis, especially among critics of measures taken by government institutions, has often highlighted a true philosophical subject, that of freedom. To what extent must my individual freedom be withdrawn from the common goods? This is a rhetorical question that comes up in any situation where security is required. As a result, cybersecurity is just as impacted.\n\nIn fact, to a lesser extent, the measures advocated by the defenders of a secure internet, block out the original vision of a libertarian internet without constraints. Do I really have to put aside certain freedoms, such as posting certain content on social networks, for the sole reason that they could possibly compromise me in the future? This choice is still up to everyone, even if in many cases it can be like playing with fire.\n\nFinally, looking at these eight examples, we see that there are many similarities between the health crisis and digital issues, which are tending to accelerate. This topic should be put on the front of the table for many experts. It makes it possible to carry out prospecting work on the basis of our experience in 2020. The benefits that could be brought to the fight against cybercrime are still diffuse. However, the management of communication and the integration of barrier gestures have been, overall, a success from which we can try to follow our example. What do you think ?",
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      "title": "Improve Cybersecurity structure with Framework NIST 1.1",
      "body": "![NIST 1.1 Cybersecurity Framework](http://01codex.com/wp-content/uploads/2021/06/NIST.jpeg)\n\n\nTo be able to implement the various actions necessary for the security of a company, the National Institute of Standards and Technology (NIST) has developed a framework dedicated entirely to cybersecurity. Since this is an open-source and independent tool, it can serve as a basis for any organization wishing to validate its cybersecurity needs.\n\n## Genesis of the NIST project\n\nSince 1901, the NIST lab has worked regularly with the government of the United States of America to analyze and propose solutions in the area of industrial competitiveness. This was created to counterbalance the great advance of Germany and the United Kingdom at the beginning of the 20th century in the fields of economy and industry.\n\nWith its great expertise in the field of physical and economic measurement, during the emergence of new technologies, NIST has been the ideal candidate to address issues related to computer security.\n\nThe lab's cybersecurity program is based on the institution's fundamentals and promotes innovation, research and the study of US competitiveness.\n\nThe subject of data and the relationship with the outside world is a crucial subject for the government of the United States. As a result, NIST's research focuses on topics such as cyberattacks, new technologies, defense methodologies and data preservation. The laboratory is very often called upon for the creation of standards and to establish rules of defense for the industry. Collaborations with the main industrial players in the country are also the responsibility of the laboratory, in order to train strategic sectors on the subject of cybersecurity.\n\n\n## A place in the conspiracy\n\nWith such a broad area of expertise, the US Federal Laboratory very often assists the US government on sensitive issues. Following the attack on the two World Trade Center towers, the laboratory was approached to analyze the possible causes of the collapse of the Twin Towers.\n\nUsing a computer simulation, bringing together an enormous amount of data, the laboratory's responsibility was to determine the multi-factorial cause. The collapse is believed to be partly due to the fragility of the load-bearing columns, following the damage caused by the impact.\n\nIn any case, this is what the computer simulation revealed after analyzing the scenarios. After this short anecdote, let's come back to the subject of the NIST framework and its importance in the analysis of a defense strategy.\n\n## How does the NIST framework work?\n\n![NIST 1.1 Cybersecurity Framework](http://01codex.com/wp-content/uploads/2021/06/CSF.png)\n\nThe NIST 1.1 Framework is made up of five so-called fundamental functions for online security. These functions are:\n\n* Identify\n* Protect\n* Detect\n* Reply\n* Restructure\n\nThese five pillars will encompass the main processes to be put in place to secure an organization using digital technologies. Each of these points is divided into sub-categories, themselves grouping together tasks to be performed.\n\nThere are also specific sub-categories offering methods of integrating solutions to common situations. Obviously each part is accompanied by reference documents and case studies.\n\nThe Framework also offers an implementation hierarchy with levels providing companies with a way to situate their skills and actions in relation to the NIST standard.\n\nFinally, the profile section will offer an overview, bringing a long-term approach. The projection on a wider horizon, favors the application of the new habits to have within a company.\n\n## An interesting database\n\nTo go further, the laboratory's website offers a fairly large database dealing with the different areas around cybersecurity. This database, dating back more than 20 years, has the advantage of bearing witness to technological advances and the associated risks since the beginning of the 21st century. It’s a real gold mine for any cybersecurity enthusiast.\n\nThe topics offered are broad and encompass:\n\n* Encryption\n* Access control\n* Risk management\n* Artificial intelligence\n* Blockchains\n* Hardware\n* Servers\n* …\n\n## Why use the NIST Framework?\n\nThere are different reasons to look into the NIST Framework 1.1. Its use will allow among other things:\n\n* Understanding the risks associated with new technologies\n* Prevent and prepare for potential threats\n* Raise awareness of all stakeholders in an organization on the safety aspect\n* Validate the security of the tools put in place by a company or an individual\n\nThe platform offers free of charge the knowledge and tools necessary to implement an effective strategy against computer threats.\n\n## Reminder\n\nThe purpose of the NIST 1.1 framework is only to present a list of recommendations and to prevent risks associated with computer technologies. Compliance with the advice provided by the institution is the responsibility of the company via a critical self-assessment of its structure. Therefore, its use should be indicative and guide a process of raising awareness and improving cybersecurity.\n\nsee more : https://01codex.com",
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      "body": "Hello welcome to Steemit world! \n I'm @steem.history, who is steem witness. \n This is a recommended post for you.[Newcomers Guide](https://steemitdev.com/guide/@steemitblog/steemit-a-guide-for-newcomers) and [The Complete Steemit Etiquette Guide (Revision 2.0)](https://steemit.com/steem/@steem.history/the-complete-steemit-etiquette-guide-revision-20-homage-1598425779) and, recommended community [Newcomers Community](https://steemit.com/trending/hive-172186) \n I wish you luck to your steemit activities.<center> \n \n \n https://cdn.steemitimages.com/DQmXHwdcNs5VPcBft1iSosPdHLpBNBfjuG84g3ffWhMw5JQ/image.png \n <sub>(The bots avatar has been created using https://robohash.org/)</sub> \n @steem.history \n \n ### My witness activity \n - [My aspiration for STEEM witness](https://steemit.com/hive-185836/@steem.history/my-aspiration-for-steem-witness-1601280729) \n - Provides information on Steem.  \n [Reference](https://steemit.com/trending/hive-130095) \n - Supporting the Steem project. \n [SPUD4STEEM project](https://steemit.com/trending/spud4steem) \n - Supporting the community. \n [Newcomers Community](https://steemit.com/trending/hive-172186),[Steem Sri Lanka](https://steemit.com/trending/hive-133716) ,[WORLD OF XPILAR](https://steemit.com/trending/hive-185836) \n \n ### My featured posts \n - [The Complete Steemit Etiquette Guide (Revision 2.0) -Homage](https://steemit.com/steem/@steem.history/the-complete-steemit-etiquette-guide-revision-20-homage-1598425779) \n \n [![image.png](https://cdn.steemitimages.com/DQmd7of2TpLGqvckkrReWahnkxMWH6eMg5upXesfsujDCnW/image.png)](https://steemlogin.com/sign/account-witness-vote?witness=steem.history&amp;approve=1) \n <sub>please click it!</sub> \n \n ![image.png](https://cdn.steemitimages.com/DQmWDnFh7Kcgj2gdPc5RgG9Cezc4Bapq8sQQJvrkxR8rx5z/image.png) \n <sub>(Go to https://steemit.com/~witnesses and type fbslo at the bottom of the page)</sub> \n \n </center>",
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      "title": "Understand The deep Q learning method : an usefull technic for AI",
      "body": "# Understand The deep Q learning method : an usefull technic for AI\n\n## Foreword\n\nOne of the most popular models in the deep reinforcement-learning sub-branch is the algorithm called Deep Q Network or DQN. Understanding this method is extremely important. It is the basis of many deep reinforcement-learning algorithms. The DQN model was first proposed by researchers at Google DeepMind in 2013 in a paper called Playing Atari with Deep Reinforcement Learning. In it, they described the DQN architecture and explained why this method was so effective especially for Atari games.\n\nIn this period, it is no exaggeration to say that the DQN method appeared in a good time. Indeed, other methods based on the Q function had made it possible to advance in the field of reinforcement learning. However, researchers remained at an impasse. At the same time, the tabular Q-learning method was on the rise and had solved some problems regarding the accumulation of states in reinforcement problems. Despite everything, it was not very effective in the context of observation of large ensembles. Therefore, from there to evoking a solution for the resolution of video games, it was inconceivable. The complexity was too great for them. Having to read all of the pixels and their states was inconceivable. The algorithms would have had too many states to consider. Tests have shown that the available reinforcement methods did not even come close to a satisfactory solution.\n\nEspecially since in some environments the number of observations is almost endless. Quickly, it was necessary to decide on the ranges of parameters to be taken into account to distinguish the crucial states from those that are not relevant. For a video game, a single pixel will not make a difference in a game. It is therefore possible to create a larger assembly as part of the image as a state. However, even so, it is necessary to be able to distinguish areas of the screen where the action is relevant to the sequence of events. One solution in this case has been to provide both a state and an action mapped into a single value. In machine learning, we talk about a regression problem. There are several concrete ways to represent and train this type of representation.\n\nHere we will describe one of them. Because of its efficiency and its ease in spreading over different problems, the Deep Q Network has become essential for reinforcement learning.\n\n## The genesis of DQN\n\nRemember that the goal of reinforcement learning as a whole is to find the optimal policy that will give us the maximum return. In order to calculate this policy, we first start by calculating the emblematic function of the DQN method, the Q function.\n\nRecall that the function Q is the function of value and state. It indicates the value of a state-action pair. Its use allows us to use the result obtained by the agent starting from a state s and executing the action “a” following a policy π.\n\nIn the case of the DQN method, we have to calculate the set of state-action values ​​with the Q function. However, in some cases it can be extremely long and complicated to calculate the Q-value of each action-state pair. Instead of calculating Q values ​​iteratively, we can use one of the many approximation functions. Here in the context of the DQN, it will be with a neural network that we will do. In this way, we can then parameterize our function Q with a parameter theta and calculate the Q value with parameter theta. We just feed the state of our environment into a neural network and it will return the Q value of all possible actions from that state. Once the Q values ​​are found, we can select the best action like the one with the maximum Q value. Since we are using a neural network to approximate the Q values, the neural network is called the Q network. Since most of the time we are dealing with a deep neural network to approximate the Q value, then our deep neural network is called a deep Q network or DQN. Now you know all about the genesis of DQN. However, at this point, many questions remain unanswered. Let us try to take it one step further.\n\n## The interactions with the environment\n\nBefore going into details about the DQN method, it is important to review the agent's interactions with its environment. We know that for the agent it is necessary to interact with the environment in order to receive rewards but also data for training. We can act randomly as is done in some cases. But in the context of a video game, for example, is it really relevant? What is the probability of winning in this scenario? It's very rare. So it will take several games before you achieve enough success. An alternative would be to use our approximate Q function as a source of behavior. We find the principle mentioned in the context of the iterative value method.\n\nIf our representation of Q is good, then the experience we get from acting on the environment will show us that the agent's data is valid for training. In a low-quality approximation, we detect it by experience. The concern here is that our agent can get caught up in bad deeds without trying to behave differently. We return to our dilemma of exploitation - exploration. It is important to give the agent the opportunity to explore the environment and build a series of transitions and actions with various outputs. Even though we should not let it act in just any manner, we are above all looking for efficiency. For example, it would be silly to reproduce actions whose sequence has already been presented and did not yield any interesting data.\n\nResearch has shown that an initially randomized system, when the approximation cannot be used, is very often an interesting alternative since it provides a basis that offers sufficient and varied interactions with the environment. Eventually, as our training system progresses, the randomized behavior becomes ineffective and we can model our approximation Q and choose what action to take. This one proposing with our training, a more serious approximation.\nA method that applies this proposition very well is the ϵ-greedy method. It will simply alternate between a random phenomenon and the policies of Q by using the hyper parameter ϵ. By varying ϵ, we choose the part of randomness in our actions. We start in practice with ϵ = 1.0, to gradually end around a value varying between 0.05 or 0.02 depending on the case. This method helps both to explore the environment at the start of the experiment, but also to maintain a good policy afterwards by switching to the Q function. There are other solutions to deal with the subject of exploration and exploitation. However, that will not be our subject here.\n\n## How to train a network in deep Q Learning ?\n\nThis is a question we can legitimately ask ourselves. Truthfully speaking, the good training of the network provides the quality of the results of the algorithm. So let us see the researchers' proposal on this subject.\n\nRecall that we run our algorithm with the network parameter θ. Its initial value is random in order to be able to start approaching the optimal Q function. Since we are in the phase of initializing the function, the result will never be optimal. We are therefore going to train the network over several iterations. We try by this principle to find the optimal parameter θ. Once the optimal parameter θ has been found, we will have the optimal Q function. Then we can extract the optimal policy from the optimal Q function.\n\nOne of the most popular solutions to find optimal θ and Q is the use of a deep neural network. We find this approach especially in the context of representation associated with images on the screen.\n\n### The training data:\n\nThe basis of Q-learning is borrowed from the supervised machine learning model. Indeed, we are trying to work with a complex nonlinear function, in our case Q with a neural network. To do this we have to calculate targets for the function using the Bellman equation to claim to have a supervised learning problem at hand. This is a good idea, but to use an optimization SGD (the little name behind this technique), our training data must be independent and identically distributed (I.I.D principle).\n\nIn our case, our data does not agree with this principle. To remedy this, we can use a buffer in which we record our past experiences and our training samples. This collection will replace the latest experiments. We are talking about the replay buffer technique. We use this buffer called replay buffer to collect the agent's experience. The replay buffer will simply serve as a collection of experiences that we set aside to use for training. From there, based on that experience, we train our network.\n\n### Replay buffer :\n\nThis tuple in our replay buffer, also called in French, experience buffer. We usually denote it with D. This information transition is what is trivially called the agent experience. The idea of using a buffer to store user experience is interesting since we train our DQN with the experience we sampled in the buffer.\n\nIn this way, we collect the agent's transition information during several episodes and we save it in the replay buffer. Transition information is stored in a stack (our replay buffer) where the newly entered data will be added to the bottom of the stack. However, this method has a disadvantage. In fact, we are going to store the experiences here one by one. From experience to experience, these will often be similar. To avoid this, we can integrate some random transition into the replay buffer before training the network.\n\nRemember that the replay buffer has a storage limit, and can therefore only keep a certain number of agent experiences. When this is filled, the new entries overwrite the old ones. The replay buffer is generally constructed like the structure of a queue. That is, so that the first entries are the first exits. Therefore, each addition after the full battery will eliminate the oldest experience and make room for the new one. At the same time, this allows the sample replay buffer to be improved. The further we go in training, the better the experiences tend to be.\n\n### Using the loss function, a great ally:\n\nIn our DQN method, we want to predict the values of Q. Now, these are continuous values. This will add complexity to the prediction. To overcome this, we use in DQN a regression task. The most popular method is the mean squared error, also noted MSE. It will come to play the role of loss function during the regression. The principle of the MSE can be translated as the squared average of the difference between the target value and the predicted value, we note that as follows:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.39.59.png)\n\nWhere y plays the role of target value, y \" of the predicted value and K the number of training samples.\n\nOur goal is to train our network and try to minimize the MSE between the target Q value and the predicted Q value as much as possible. Obviously, the goal is to get the optimal Q value, which also comes down to minimizing error. Since the difference between the target Q value and the current Q value tends towards 0 as one approaches the optimal Q value. We use the Bellman equation for this. You will find an article covering the concept of this equation here. We know that the optimal Q value can be obtained using the Bellman Optimal equation:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.49.16.png)\n\nWhere R (s, a, s ’) represents the immediate reward r, obtained by performing an action in state s leading to state s’. It is therefore possible to replace R (s, a, s ’) simply by r.\n\nIn our equation, we can also remove the expected factor E. Indeed, we will approximate this factor by taking a sample of K transitions from the replay buffer and recovering an average value. Thus according to the optimal Bellman equation, the optimal value Q is the sum of the rewards to which we add the maximum value Q reduced to the next action-state pair:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.52.04.png)\n\nWe can also define our loss as the difference between the target value (the optimal Q value) and the predicted value (the Q value predicted by DQN). The loss function will then be expressed:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.55.13.png)\n\nBy substituting the above equation for the previous one, we get this simplified formula:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.55.53.png)\n\nWe calculated the predicted Q-value using the network parameter θ. Now let's see how to calculate the target value. We have seen that it is the sum of the rewards to which we add the maximum Q value reduced to the next action-state pair.\n\nSimilar to the predicted Q-value, we can calculate the Q-value of the next action-state pair by using the same network parameter, θ. Indeed, notice in our equation that we have, the two values Q parameterized by θ.\n\nInstead of calculating the loss as just the difference between the target Q value and the predicted Q value, we use the MSE method as the loss function. Remember that we have our experiences stored in the replay buffer. In addition, we have a small part of our randomized sample. We then train the network and try to minimize the MSE. We can then translate our loss function as follows:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.56.38.png)\n\nWe have seen that the target value is simply the sum of the rewards and the maximum Q value reduced to the next action-state pair. Consider the case where state s is the terminal state. If s is the last step in an episode, then we cannot calculate the Q value, since we have no action to take in the terminal state. In this case, the target value will become our reward. Therefore, our loss function will look like:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.57.19.png)\n\nWe have trained our network by minimizing the loss function. However, we have seen that it is also possible to minimize the loss function by finding the optimal parameter θ. To do this, we can use the gradient descent method and find the optimal parameter θ (how can we reduce the MSE with the gradient descent?).\n\n\n### Correlation between stages:\n\nAnother problem with our Q-learning model concerns its training procedure. By default, it is based on a method that does not respect the i.i.d principle. The Bellman equation gives us values of Q (s, a) in direct relation to Q (s ’, a’) (we speak of bootstrapping). The concern being that between s and s, only one step has passed. This makes s and s very similar, too similar in fact. This makes their differentiation very difficult for the neural networks. So when we optimize our neural network parameters, to achieve a Q (s, a) value close to the desired result, we can quickly alter the value of Q (s ’, a’) with each iteration. This will make our training very unstable. We have an error propagation which can completely destroy our approximation of Q (s, a).\n\nThere is a trick to solve this problem. We can build a new network playing the role of the target. With this, we create a copy of our network that we will use for Q (s ’, a’) in the Bellman equation. This network is synchronized with the main network. We regularly and periodically synchronize the two networks. At every N step with N that will become one of DQN's hyperparameters, often between 1k and 10k.\n\n### The target network to the rescue\n\nDespite the steps already taken, we continue to have a little problem with our loss function. We know that the target value is the sum of the rewards plus the reduced maximum Q value at the next state-action pair. We calculate this Q value on the target and we predict the Q value using the same parameter θ. Now we have a problem since the target and predicted value both depend on the same parameter, in this case θ. This can cause instabilities when using the MSE and the networks will learn in a bad manner. It also causes a lot of discrepancy in training.\n\nHow can we avoid this? It is possible to freeze the target value for a time and calculate only the predicted value. To do this, we introduce another neural network called target network to calculate the Q value of the next state-action pair. The parameter of the target network is denoted θ '. Therefore, our main Q network will calculate the predicted Q values and learn the optimal θ parameter using the gradient descent method. The target network will be put on standby for a while in order to, after a period, resume operation and update the parameter θ ' by simply copying the parameter θ from the main network. We start to freeze the target network and so on. In this case, our loss function becomes:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-14.59.32.png)\n\nThe Q value of the next action state is calculated by the target network with the parameter θ 'and the predicted Q value is calculated by the main network with the parameter θ. If we go back to our simplified notation, we get:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-15.00.53.png)\n\n## The Markov property applied to Deep Q Learning\n\nOur DQ-learning method uses the formalism of the Markovian decision process. Which in principle specifies that the model obeys the Markov properties: According to this, the observations of the environment are all distributed in an optimal way. That is, each observation allows us to distinguish the entirety of the states. By analyzing a single image from a video game, we cannot retrieve all of the information. For example, the speed or direction of an object will not be known. This invalidates the Markov properties. Therefore, our system, in the case of video games, is relegated to the rank of Partial Markovian or MDPS or POMDPS decision process. We speak of POMDPS when we have a model with a Markovian decision process that does not respect the Markov properties. This method appears very regularly in a real context, such as in card games where the opponent's hand is unknown. There is a way to turn a POMDPS into an MDP.\n\n## Results\n\nAfter introducing so much new information and technical mathematical concepts, let us go back to the key elements for the DQN model.\n\nFirst, we update the general lattice parameter θ with a random value. We have learned that the target network is a simple copy of the general network. Then we update the parameter θ ' by copying the parameter θ. We also update D, our replay buffer.\n\nNow for each step in an episode we add the state of the environment in our network, and we get its outputs corresponding to the Q values for all possible actions in the state. Then we choose the action, which has the maximum Q value.\n\nIf we only select the stock with the highest Q value, then we don't explore other new stocks. To avoid this, we select our stocks by adding the ϵ-greedy policy. We select our random action with an epsilon probability and with the probability ϵ-1, we select the best action with the maximum Q value.\n\nSince we update our lattice parameter θ with a random value, the action we select by taking the maximum Q value will not necessarily be the optimal action. But that's okay, we're just improving the selected action. We then go to the next state and get the reward. If the action is good then we will receive a positive reward, otherwise, it will be negative. We store this information in replay buffer D.\n\nThen, in a random manner, we create a sample of K of transitions coming from the replay buffer for which we have previously calculated the loss parameter. Our loss function is given by:\n\n![Image of Yaktocat](http://01codex.com/wp-content/uploads/2021/05/Capture-decran-2021-05-30-a-15.07.19.png)\n\nDuring the first iterations, the loss will be very high since our parameter θ of the network is configured randomly. To minimize the loss, we calculate the gradients of the losses and we improve our network parameter θ by following the principle of the gradient descent.\n\nWe do not change the target network parameter θ' at each step. We freeze the parameter θ' for several steps and then we inject the value of θ into θ'. We repeat this process for several episodes to approximate the optimal Q value. Once the optimal Q value is found, we extract the optimal policy.\n\nThe DQN process is certainly a process requiring an assimilation of all the steps that make up the method, it remains one of the most popular and effective reinforcement learning techniques. You will find it regularly in different variations during your research.\n\nLet see more : https://01codex.com/",
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