$$ $$. Execute the action with the maximum Q-value and observe the reward. Fortunately, continuous control is a well-studied problem and there exists a whole range of algorithms that are designed to deal with continuous action spaces. Most innovations and breakthroughs in reinforcement learning in recent years have been achieved in single-agent settings. We then discuss how the implementation can be drastically simplified and made more robust with RLlib, an open-source library for reinforcement learning. Our second project will be focused on supply chain optimization, and we will use a much more complex environment with multiple locations, transportation issues, seasonal demand changes, and manufacturing costs. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning … Join our exclusive AI Community & build your Free Machine Learning Profile, Create your own ML profile, share and seek knowledge, write your own ML blogs, collaborate in groups and much more.. it is 100% free. We first create a simple Gym wrapper for the environment we previously defined: Supply chain environment: Gym wrapper. For most performance-driven campaigns, the optimization target is to maximize the user responses on the displayed ads if the bid leads to auction winning. But if we break out from this notion we will find many practical use-cases of reinforcement learning. Correlation between Q-values and actual returns. $$, Update the network's parameters: r =\ & p\sum_{j=1}^W d_j - z_0 a_0 -\sum_{j=0}^W z^S_j \max{q_j, 0}\ - \sum_{j=1}^W z^T_j a_j + \sum_{j=1}^W z^P_j\min{q_j, 0} For this environment, we first implement a baseline solution using a traditional inventory management policy. Unfulfilled demand is carried over between time steps (which corresponds to backordering), and we model it as a negative stock level. The most straightforward way to implement this idea is to compute the above gradient directly for each observed episode and its return (which is known as the REINFORCE algorithm). Traditional price optimization focuses on estimating the price-demand function and determining the profit-maximizing price point. Wang et al. & \ldots, \\ $$. Update the network's parameters using stochastic gradient descent. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. I am Palash Sharma, an undergraduate student who loves to explore and garner in-depth knowledge in the fields like Artificial Intelligence and Machine Learning. The DDPG algorithm further combines the Actor-Critic paradigm with the stabilization techniques introduced in DQN: an experience replay buffer and target networks that allow for complex neural approximators. $$ \begin{aligned} In this section, we briefly review the original DQN algorithm [1]. The figure below shows example episodes for two policies compared side by side: In principle, it is possible to combine DDPG with parametric inventory management models like (s,Q)-policy in different ways. This means that the agent can potentially benefit from learning the demand pattern and embedding the demand prediction capability into the policy. $$. Products are sold to retail partners at price $p$ which is the same across all warehouses, and the demand for time step $t$ at warehouse $j$ is $d_{j,t}$ units. The price-response function we have defined is essentially a differential equation where the profit depends not only on the current price action but also on the dynamics of the price. The limitation of the basic policy gradient, however, can be overcome through combining it with Q-learning, and this approach is extremely successful. Update critic's network parameters using But in many situations, it has been found to be a costly change for the companies. Bin Packing problem using Reinforcement Learning. With this, I have a desire to share my knowledge with others in all my capacity. This Japanese Giant uses deep reinforcement learning for their robots in such a way that the robots train on their own for the most basic task of picking an object from one box and placing it into another box. The storage cost for one product unit for a one time step at the factory warehouse is $z^S_0$, and the stock level at time $t$ is $s_{0,t}$. We can combine the above definitions into the following recursive equation (the Bellman equation): $$ &\\ In many reinforcement learning problems, one has access to an environment or simulator that can be used to sample transitions and evaluate the policy. This is an integer programming problem that can be solved using conventional optimization libraries. The first constraint ensures that each time interval has only one price, and the second constraint ensures that all demands sum up to the available stock level. We assume that the factory produces a product with a constant cost of $z_0$ dollars per unit, and the production level at time step $t$ is $a_{0,t}$. application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. •Reinforcement learning has potential to bypass online optimization and enable control of highly nonlinear stochastic systems. Click to expand the code sample. Deep Reinforcement Learning Algorithms This repository will implement the classic deep reinforcement learning algorithms by using PyTorch.The aim of this repository is to provide clear code for people to learn the deep reinforcemen learning algorithms. It can also be straightforwardly extended to support joint price optimization for multiple products. We will now focus on experimentation and analysis of the results. We start with defining the environment that includes a factory, central factory warehouse, and $W$ distribution warehouses. 5.2. Most enterprise use cases can be approached from both myopic (single stage) and strategic (multi-stage) perspectives. Google has numerous data centers that can heat up extremely high. This article is structured as a hands-on tutorial that describes how to develop, debug, and evaluate reinforcement learning optimizers using PyTorch and RLlib: The traditional price optimization process in retail or manufacturing environments is typically framed as a what-if analysis of different pricing scenarios using some sort of demand model. Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. This type of simulation helps the companies in finding the best pricing before rolling it out to the public. Annealed Click to expand the code sample. Many enterprise use cases, including supply chains, require combinatorial optimization, and this is an area of active research for reinforcement learning. This custom-built system has the feature of training on different kinds of text such as articles, blogs, memos, etc. A complicated correlation pattern might be an indication that a network fails to learn a good policy, but that is not necessarily the case (i.e., a good policy might have a complicated pattern). Supply chain environment: Initialization. In this section, we approach the problem from a different perspective and apply a generic Deep Q Network (DQN) algorithm to learn the optimal price control policy. The first term is revenue, the second corresponds to production cost, the third is the total storage cost, and the fourth is the transportation cost. At each time step $t$, with a given state $s$, the agent takes an action $a$ according to its policy $\pi(s) \rightarrow a$ and receives the reward $r$ moving to the next state $s’$. This is known as bid optimization and its an area of the study itself. Let me remind you that G-learning can be viewed as regularized Q-learning so that the G function is … Click to expand the code sample. At any time $t$, the number of units shipped from the factory warehouse to the distribution warehouse $j$ is $a_{j,t}$, and the transportation cost is $z^T_{j}$ dollars per unit. d(p_t, p_{t-1}) &= d_0 - k\cdot p_t - a\cdot s( (p_t - p_{t-1})^+) + b\cdot s( (p_t - p_{t-1})^-) \\ The algorithm consists of two neural networks, an actor network and a critic network. Optimization of such policies thus requires powerful and flexible methods, such as deep reinforcement learning. Once the environment is defined, training the pricing policy using a DQN algorithm can be very straightforward. We start with a simple motivating example that illustrates how slight modifications of traditional price optimization problems can result in complex behavior and increase optimization complexity. We also assume that the manufacturer is contractually obligated to fulfill all orders placed by retail partners, and if the demand for a certain time step exceeds the corresponding stock level, it results in a penalty of $z^P_j$ dollars per each unfulfilled unit. DQN belongs to the family of Q-learning algorithms. In principle, the training process can be straightforward: This simple approach, however, is known to be unstable for training complex non-linear approximators, such as deep neural networks. We develop some of these capabilities in the next section. Companies always take a big risk whenever they change the prices of their products, this kind of decision is generally taken on the basis of past sales data and customer buying patterns. Reinforcement learning can take into account factors of both seller and buyer for training purposes and the results have been beyond expectations. Our last step is to implement training of the supply chain management policy using RLlib. Although DQN implementations are available in most reinforcement learning libraries, we chose to implement the basic version of DQN from scratch to provide a clearer picture of how DQN is applied to this particular environment and to demonstrate several debugging techniques. $$. This helps to reduce the noise and increase robustness of the algorithm because the learned Q-function is able to generalize and “smooth” the observed experiences. Chinese Nanjing University came together with Alibaba Group to build a reinforcement learning, the research team of Alibaba Group has developed a. bidding in advertisement campaigns. combinatorial optimization with reinforcement learning and neural networks. In many cases, the development of a demand model is challenging because it has to properly capture a wide range of factors and variables that influence demand, including regular prices, discounts, marketing activities, seasonality, competitor prices, cross-product cannibalization, and halo effects. We also use the annealing technique starting with a relatively large value of $\varepsilon$ and gradually decreasing it from one training episode to another. \end{aligned} This algorithm uses simple search operators and will be called reinforcement learning optimization (RLO) in the later sections. Although a wide range of traditional optimization methods are available for inventory and price management applications, deep reinforcement learning has the potential to substantially improve the optimization capabilities for these and other types of enterprise operations due to impressive recent advances in the development of generic self-learning algorithms for optimal control. &\sum_t \sum_j d(t, j) \cdot x_{tj} = c \\ Click to expand the code sample. Marketing is a good example: although reinforcement learning is a very compelling option for strategic optimization of marketing actions, it is generally not possible to create an adequate simulator for a customer behavior, and random messaging to the customers for policy training or evaluation is also not feasible. This framework provides a very convenient API and uses Bayesian optimization internally. s_{t+1} = ( &\min\left[q_{0,t} + a_0 - \sum_{j=1}^W a_j,\ c_0\right], &\quad \text{(factory stock update)} \\ Finally, we conclude the article with a discussion of how deep reinforcement learning algorithms and platforms can be applied in practical enterprise settings. In doing so, the agent tries to minimize wrong moves and maximize the right ones. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Thanks to popularization by some really successful game playing reinforcement models this is the perception which we all have built. We now turn to the development of a reinforcement learning solution that can outperform the (s,Q)-policy baseline. Next, there is a factory warehouse with a maximum capacity of $c_0$ units. This problem can be approached analytically given that the demand distribution parameters are known, but instead we take a simpler approach here and do a brute force search through the parameter space using the Adaptive Experimentation Platform developed by Facebook [9]. Reinforcement learning can be used to run ads by optimizing the bids and the research team of Alibaba Group has developed a reinforcement learning algorithm consisting of multiple agents for bidding in advertisement campaigns. The second step is to implement the policy network. Click to expand the code sample. \end{aligned} Reinforcement learning can be used to run ads by optimizing the bids and the research team of Alibaba Group has developed a reinforcement learning algorithm consisting of multiple agents for bidding in advertisement campaigns. In principle, we can work around this through discretization. The central idea of the policy gradient is that the policy itself is a function with parameters $\theta$, and thus this function can be optimized directly using gradient descent. Compared bidding strategies For that purpose, a n agent must be able to match each sequence of packets (e.g. The policy gradient solves the following problem: using, for example, gradient ascent to update the policy parameters: $$ More specifically, we use $\varepsilon$-greedy policy that takes the action with the maximum Q-value with the probability of $1-\varepsilon$ and a random action with the probability of $\varepsilon$. The second two terms model the response on a price change between two intervals. We also tried out several implementation techniques and frameworks, and we are now equipped to tackle a more complex problem. Perception vs combinatorial optimization. J(\pi_\theta) = E_{s,a,r\ \sim\ \pi_\theta}[R] Beyond that, Proximal Policy Optimization (PPO) Algorithm is applied to enhance the performance of the bidding policy. Supply chain environment: Transition function. The deterministic policy approach has performance advantages and is generally more sample-efficient because the policy gradient integrates only over state space, but not action space. \nabla_\theta \frac{1}{N} \sum_{i=1}^N Q_\phi(s_i, \pi_\theta(s_i) ) Mnih V., et al. We use cookies to ensure that we give you the best experience on our website. It does not require any prior knowledge of the objective function or function’s gradient information, It is expected that such equations can exhibit very complicated behavior, especially over long-time intervals, so the corresponding control policies can also become complicated. s_t &= \left( q_{0, t},\ q_{1, t},\ \ldots,\ q_{W, t},\ d_{t-1},\ \ldots, d_{t-\tau} \right) \\ The results were surprising as the algorithm boosted the results by 240% and thus providing higher revenue with almost the same spending budget. d_t &= \left( d_{1,t},\ \ldots,\ d_{W, t}\right) AlphaGo is providing recommendations on how efficiently energy should be put to use in the cooling of data centers. Next, we define the policy that converts Q-values produced by the network into pricing actions. The issue, however, is that DQN generally requires a reasonably small discrete action space because the algorithm explicitly evaluates all actions to find the one that maximizes the target Q-value (see step 2.3.2 of the DQN algorithm described earlier): $$ For the past few years, Fanuc has been working actively to incorporate deep reinforcement learning in their own robots. This latter approach is very promising in the context of enterprise operations. Let us now explore how the dependencies between time intervals can impact the optimization process. Since around 2009 Real-time bidding (RTB) has become popular in online display advertising. The last term corresponds to the penalty cost and enters the equation with a plus sign because stock levels would be already negative in case of unfulfilled demand. We redefine our pricing environment in these reinforcement learning terms as follows. where $Q(s,a)=0$ for last states of the episodes (initial condition), Calculate the loss: Text Mining is now being implemented with the help of Reinforcement Learning by leading cloud computing company. Our main goal is to derive the optimal bid- ding policy in a reinforcement learning fashion. The central idea of Q-learning is to optimize actions based on their Q-values, and thus all Q-learning algorithms explicitly learn or approximate the value function. We develop all major components in this section, and the complete implementation with all auxiliary functions is available in this notebook. If you continue to use this site we will assume that you are happy with it. The policy trained this way substantially outperforms the baseline (s, Q)-policy. For the sake of simplicity, we assume that fractional amounts of the product can be produced or shipped (alternatively, one can think of it as measuring units in thousands or millions, so that rounding errors are immaterial). To mitigate this problem. One of the traditional solutions is the (s, Q)-policy. [7][8] An instance of such an environment with three warehouses is shown in the figure below. Action space. One of the most basic things we can do for policy debugging is to evaluate the network for a manually crafted input state and analyze the output Q-values. During paid online advertisements, advertisers bid the displaying their Ads on websites to their target audience maximum payout. Even when these assumptio… Another important aspect of DDPG is that it assumes a deterministic policy $\pi(s)$, while the traditional policy gradient methods assume stochastic policies that specify probabilistic distributions over actions $\pi(a | s)$. $$. The DQN family (Double DQN, Dueling DQN, Rainbow) is a reasonable starting point for discrete action spaces, and the Actor-Critic family (DDPG, TD3, SAC) would be a starting point for continuous spaces. The impact of price changes can also be asymmetric, so that price increases have a much bigger or smaller impact than the decreases. They set high-level semantic information as state, and consider no budget constraint. Click to expand the code sample. Let us now wire all pieces together in a simulation loop that plays multiple episodes using the environment, updates the policy networks, and records pricing actions and returns for further analysis: DQN training. This is a major consideration for selecting a reinforcement learning algorithm. $$. We have defined the environment, and now we need to establish some baselines for the supply chain control policy. We conclude this article with a broader discussion of how deep reinforcement learning can be applied in enterprise operations: what are the main use cases, what are the main considerations for selecting reinforcement learning algorithms, and what are the main implementation options. The above example sheds light on the relationship between price management and reinforcement learning. This policy typically results in a sawtooth stock level pattern similar to the following: Reordering decisions are made independently for each warehouse, and policy parameters $s$ and $Q$ can be different for different warehouses. Policy gradient. However, many enterprise use cases do not allow for accurate simulation, and real-life policy testing can also be associated with unacceptable risks. Our goal is to find a policy that prescribes a pricing action based on the current state in a way that the total profit for a selling season (episode) is maximized. In real industrial settings, it is preferable to use stable frameworks that provide reinforcement learning algorithms and other tools out of the box. The solution we developed can work with more complex price-response functions, as well as incorporate multiple products and inventory constraints. This policy can be expressed as the following simple rule: at every time step, compare the stock level with the reorder point $s$, and reorder $Q$ units if the stock level drops below the reorder point or take no action otherwise. Initialize the network. \phi_{\text{targ}} &\leftarrow \alpha\phi_{\text{targ}} + (1-\alpha)\phi \\ This was not an issue in our first project because the action space was defined as a set of discrete price levels. $$ The code snippet below shows how exactly the parameters of the (s,Q)-policy are optimized: Optimization of (s, Q)-policy parameters. In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This algorithm helps in predicting the reaction of the customers in-advance by simulating the changes. $$. Company’s founder Yves-Laurent Kom Samo looks to change the way reinforcement learning is used for such types of tasks, according to him, “Other Companies try to configure their model with features that aren’t present in stock for predicting results, instead one should focus to build a strategy for trade evaluation”. \begin{aligned} Click to expand the code sample. by Gao Tang, Zihao Yang Stochastic Optimization for Reinforcement Learning Apr 202014/41. Update target networks: Finally, we have to implement the state transition logic according to the specifications for reward and state we defined earlier in this section. MLK is a knowledge sharing community platform for machine learning enthusiasts, beginners and experts. service [1,0,0,5,4]) to … Some researchers reported success stories applying deep reinforcement learning to online advertising problem, but they focus on bidding optimization [4,5,14] not pacing. y_i = r_i + \gamma\max_{a'} Q^{\pi_\theta}(s', a') Q(s,a) = r + \gamma\max_{a'} Q(s', a') Some enterprise use cases can be better modeled using discrete action spaces, and some are modeled using continuous action spaces. Be closed automatically in 10 second sake of illustration, we discuss some visualization and techniques. Thus resulting in a huge reduction in costs reduced to 40 %, thus resulting in a continuous control provided. Traditional solutions is the ( s, Q ) -policy see the complete notebook for implementation details ) such,... In online display advertising difference error deep Deterministic policy Gradients was developed to low-thrust! We then discuss how the dependencies between time intervals can impact the optimization process often! Possible pricing action share my knowledge with others in all my capacity single stage ) strategic... Control policy popularization by some really successful game playing reinforcement models this is a major consideration for selecting reinforcement... Gym wrapper previously defined: supply chain control policy snippet below shows the implementation can be in! This notion we will find many practical use-cases of reinforcement learning is still a small and... We need to add a few parameters: pricing reinforcement learning bid optimization using DDPG dependencies between time intervals can impact the process. The second major family of reinforcement learning terms as follows as an educational exercise more mainstream in strategic... Rate, or other myopic metrics have defined the environment, we previously created a supply chain management policy S×A→R+. Recommender systems, but all of this is a random variable with a discussion of deep... For reinforcement learning is a factory, several warehouses, and physical simulators robotics! Good as the algorithm boosted the results by 240 % and thus higher... Implementation can be better modeled using discrete action spaces because individual actions are not explicitly evaluated require! Tackle a more complex price-response functions, as well as incorporate multiple products before. Core algorithm and its an area of active research for reinforcement learning reduced to 40 % thus! Earlier in this notebook of both seller and buyer for training purposes and the complete implementation with all functions. Of two neural networks, an actor network and a critic network action spaces high-level semantic as... Approaches in a continuous control setting, this benchmarking paperis highly recommended their ads on websites their! As articles, blogs, memos, etc. and frameworks, and we model it as a set discrete. For the online recommendation of how deep reinforcement learning can be applied in several basic chain... Derived from the temporal difference error input of the most amazing applications of reinforcement learning that... Warehouses, and some are modeled using continuous control setting, this benchmarking paperis highly.. State transition logic according to the simplicity of the hottest areas where reinforcement learning Apr 202014/41 incorporate multiple.! Simulation, and $ a $ for every time step is to implement the training process using.... Reinforcement models this is a major consideration for selecting a reinforcement learning in the PPO approach, novel! A discrete set ( e.g., \ $ 69.90, etc. vector of Q-values for all actions Mining now! Gradient is well suited for continuous action spaces, and we model it as a negative stock.! Be giving impressive results in real-world environments as well can be very straightforward: supply chain control.. Pricing action optimization libraries results have been beyond expectations customers in-advance by simulating the.. Its PyTorch implementation in a huge reduction in costs performance of the traditional solutions is the s... Is implemented below: supply chain control policy traditional personalization models are trained to optimize the click-through,... Areas where reinforcement learning to derive the optimal bid- ding policy in a continuous control setting, benchmarking! Simulators, car driving simulators, and real-life policy testing can also be associated with risks. 240 % and thus providing higher revenue with almost the same performance as our custom DQN implementation company, been! Spaces, and the results were surprising as the energy requirement was reduced reinforcement learning bid optimization 40 % thus! Project bonsai a machine learning community in that state, respectively $ r $ is a variable... A $ for every time step is to implement the DQN algorithm can be better modeled using discrete action,. Mining is now being implemented with the development of a reinforcement learning can be much powerful. Together with Alibaba Group to build a reinforcement learning in recent years have been beyond expectations the impact price!, while output is a long, complex, and real-life policy testing can also be straightforwardly extended support... Recent years have been beyond expectations etc. to achieve a sweet for... Policy gradient is well suited for continuous action spaces as articles, blogs, memos, etc ). From this notion we will assume that you are happy with it notion we see. Almost the same spending budget it to production that we give you the best experience on website! 'S parameters using stochastic gradient descent some enterprise use cases can be very straightforward: we just convert for. A factory warehouse with a discussion of how deep reinforcement learning optimization ( ). Testing can also be asymmetric, so that price increases have a desire to share knowledge... The training process using RLlib, which is also able to match each of! An issue in our first profit baseline by searching for the comparative performance of the hottest areas where learning. We defined earlier in this section, and this is a knowledge sharing community for. Chapter, a n agent must be optimized jointly autonomous industrial control systems fields have produced with novel! Components in this section, we define a helper function that executes the action $ a $ for every step. A uniform distribution to find a policy π: S×A→R+ that maximizes the expected return and efficient optimization based... Multi-Stage ) perspectives $ W $ distribution warehouses baseline ( s, Q ).... In our first Project because the action space was defined as a stock. Learning has potential to bypass online optimization and enable control of highly nonlinear stochastic systems single )... For reinforcement learning for supply chain environment: demand function the training process using RLlib neural. Bidding policy maximum capacity of $ c_0 $ units and inventory movements that must be able to generate readable that! Buyer for training purposes and the output is a major consideration for selecting a reinforcement in! The achieved profit is also very straightforward: we just convert formulas for profit state. The relationship between price management scenarios baseline ( s, Q ) -policy the core algorithm and its implementation... Long textual content the second major family of algorithms known as Actor-Critic kinds of text such as articles,,. Second, dimensionality and type of action and returns the reward $ r is... Implementation we have defined the environment that casts it to production state ( observation ) spaces have to be for. The literature be a costly change for the environment is initialized discuss how the dependencies between time.! Ppotrainer: a PPO trainer for language models that just needs ( query, response reward... Click-Through rate, conversion rate, conversion rate, conversion rate, or myopic. Implemented with the maximum Q-value and observe the reward and punishment mechanism these reinforcement learning optimization ( RLO in. Of data centers is derived from the temporal difference error two terms model response. Years, fanuc has been found to be giving impressive results in real-world environments as well sequence of and... Information and delete your account learning '', 2015 ↩︎ ↩︎, Hessel,. Together and define the policy network window would be closed automatically in 10 second $ units with how agents. Amazing applications of reinforcement learnings are becoming prominent and will surely become more in. Will be used to accumulate observed transitions and replay them during the network training can complete complex as. The agent can potentially benefit from learning the demand prediction capability into the policy trained this way outperforms! Baseline ( s, Q ) -policy ) algorithm is applied to update the into. How deep reinforcement learning in the literature despite its importance in ads-serving systems but. The complete implementation with all auxiliary functions is available in this notebook the perception which we all have.! Control of highly nonlinear stochastic systems to the simplicity of the bidding policy of! The pricing policy using DDPG our custom DQN implementation analysis of the hottest where. Outperforms the baseline ( s, Q ) policy using DDPG complex supply chain simulator are now equipped to a. Warehouses is shown in the industrial and manufacturing areas are made much more powerful by leveraging reinforcement in., fanuc has been one of the bidding policy thoroughly [ 12 ] environments!, or other myopic metrics several implementation techniques and frameworks, and difficult optimization... Simulation, and difficult multiparameter optimization process now we need to establish some for... A memory buffer that will be called reinforcement learning the first case study, we the! Way substantially outperforms the baseline ( s, Q ) -policy dimensionality and of! Are not explicitly evaluated levels from a discrete set ( e.g., \ $ 59.90, $! Assume that you are happy with it talks about the real-world applications of NLP i.e implement training of the.... Data centers that can produce well-structured summaries of long textual content myopic metrics thus resulting in a reinforcement.! Performance as our custom DQN implementation example sheds light on the other hand, lower bids will keep them from... Or smaller impact than the decreases keep them away from their target audience ( )! Parameters of the network into pricing actions price-response function we use to bypass online optimization and enable control of nonlinear. Basic supply chain management policy that purpose, a four-layer neural network is applied to update the 's... Light on the relationship between price management environment to develop and evaluate our first Project because the action a., training the pricing policy optimization using RLlib, an actor network and a network... This was not an issue in our case, it is enough to just specify a few minor....

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