reinforcement learning bid optimization

DDPG also uses soft updates (incremental blending) for the target networks, as shown in step 2.3.5, while DQN uses hard updates (replacement). Our next step is to implement the DQN algorithm using PyTorch [5]. MLK is a knowledge sharing community platform for machine learning enthusiasts, beginners and experts. Let us now explore how the dependencies between time intervals can impact the optimization process. 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]. \max \ \ & \sum_t \sum_j p_j \cdot d(t, j) \cdot x_{tj} \\ Setting policy parameters represents a certain challenge because we have 8 parameters, i.e., four (s,Q) pairs, in our environment. combinatorial optimization with reinforcement learning and neural networks. This concludes our basic DQN implementation. Finally, we conclude the article with a discussion of how deep reinforcement learning algorithms and platforms can be applied in practical enterprise settings. $$. Analytic gradient computation Assumptions about the form of the dynamics and cost function are convenient because they can yield closed-form solutions for locally optimal control, as in the LQR framework. In this article, we will see some of the most amazing applications of reinforcement learning that you did not know exist. The policy gradient solves the following problem: using, for example, gradient ascent to update the policy parameters: $$ During paid online advertisements, advertisers bid the displaying their Ads on websites to their target audience maximum payout. This leads to the third family of algorithms known as Actor-Critic. Consequently, our next step is to reimplement the same optimizer using RLlib, an open-source library for reinforcement learning developed at the UC Berkeley RISELab [6]. A reinforcement learning (RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with a Xilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. service [1,0,0,5,4]) to … Click to expand the code sample. 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. 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. For instance, we previously created a supply chain simulator. “Reinforcement learning for supply chain optimization,” 2018 ↩︎, Oroojlooyjadid A., et al. Update actor's network parameters using Click to expand the code sample. 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 will now focus on experimentation and analysis of the results. First, the environment needs to fully encapsulate the state. 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. & \min\left[q_{1, t} + a_{1, t} - d_{1, t},\ c_1 \right], &\quad \text{(warehouse stock update)} \\ RTB allows an application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. 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. Many of these frameworks provide only algorithm implementations, but some of them are designed as platforms that are able to learn directly from system logs and essentially provide reinforcement learning capabilities as a service. Q^{\pi}(s,a) = r + \gamma\max_{a'} Q(s', a') Technical platform. In the real world, demand depends not only on the absolute price level but can also be impacted by the magnitude of recent price changes—price decrease can create a temporary demand splash, while price increase can result in a temporary demand drop. 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). Coefficients $a$ and $b$ define the sensitivity to positive and negative price changes, respectively, and $s$ is a shock function that can be used to specify a non-linear dependency between the price change and demand. The limitation of the basic policy gradient, however, can be overcome through combining it with Q-learning, and this approach is extremely successful. The following plot shows how returns change during the training process (the line is smoothed using a moving average filter with a window of size 10; the shaded area corresponds to two standard deviations over the window): The learning process is very straightforward for our simplistic environment, but policy training can be much more difficult as the complexity of the environment increases. & \min\left[q_{W, t} + a_{W, t} - d_{W, t},\ c_W \right], &\quad \text{(warehouse stock update)} \\ Assuming that a retailer chooses pricing levels from a discrete set (e.g., \$59.90, \$69.90, etc.) Traditional personalization models are trained to optimize the click-through rate, conversion rate, or other myopic metrics. Tech Giant Google has leveraged reinforcement learning in the most unique way. $$. But in many situations, it has been found to be a costly change for the companies. 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. The success of deep reinforcement learning largely comes from its ability to tackle problems that require complex perception, such as video game playing or car driving. Generally, high bids get the best spot and target audience but advertisers cannot afford to bid very high to deplete their overall returns from ads. Click to expand the code sample. In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. Most enterprise use cases can be approached from both myopic (single stage) and strategic (multi-stage) perspectives. For example, the autonomous forklift can be trained to align itself with a pallet, lift the pallet, put it down, all with the help of their reinforcement learning platform. Click to expand the code sample. In such cases, one has to learn offline-based historical data and carefully evaluate a new policy before deploying it to production. The applicability of deep reinforcement learning to traditional combinatorial optimization problems has been studied as well, but less thoroughly [12]. In our case, it is enough to just specify a few parameters: Pricing policy optimization using RLlib. For instance, consider an apparel retailer that purchases a seasonal product at the beginning of the season and has to sell it out by the end of the period. To mitigate this problem, Google uses AlphaGo built by DeepMind, for figuring out the optimal method that can help in designing the cooling infrastructure. GPT2 model with a value head: A transformer model with an additional scalar output for each token which can be used as a value function in reinforcement learning. Tech Giant Google has leveraged reinforcement learning in the most unique way. If we retrain the policy with $\gamma=0.80$, then the distribution of Q-values for the same input state will be substantially different and price surges will be articulated better: Note that the absolute values of the Q-function do not match the actual return in dollars because of discounting. Our supply chain environment is substantially more complex than the simplistic pricing environment we used in the first part of the tutorial, but, in principle, we can consider using the same DQN algorithm because we managed to reformulate the problem in reinforcement learning terms. Chapter 5: Deep Reinforcement Learning This chapter gives an understanding of the latest field of Deep Reinforcement Learning and various algorithms that we intend to use. 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. \pi(s) = \underset{a}{\text{argmax}}\ Q(s,a) \theta \leftarrow \theta + \alpha \nabla_\theta J(\pi_\theta) The implementation is straightforward, as it is just a generic cyclic buffer: Experience replay buffer. Most innovations and breakthroughs in reinforcement learning in recent years have been achieved in single-agent settings. We first create a simple Gym wrapper for the environment we previously defined: Supply chain environment: Gym wrapper. We redefine our pricing environment in these reinforcement learning terms as follows. 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. With this, I have a desire to share my knowledge with others in all my capacity. &\\ This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. Actor-Critic: Combining policy gradient with Q-learning. If you continue to use this site we will assume that you are happy with it. Nonzero-Sum Game Reinforcement Learning for Performance Optimization in Large-Scale Industrial Processes Abstract: This article presents a novel technique to achieve plant-wide performance optimization for large-scale unknown industrial processes by integrating the reinforcement learning method with the multiagent game theory. 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”. The constant-price schedule is not optimal for this environment, and we can improve profits through greedy optimization: start with finding the optimal price for the first time step, then optimize the second time step having frozen the first one, and so on: Price optimization: Dynamic price. The chart shows that TD errors are reasonably small, and the Q-values are meaningful as well: Finally, it can be very useful to visualize the correlation between Q-values and actual episode returns. The above model is quite flexible because it allows for a price-demand function of an arbitrary shape (linear, constant elasticity, etc.) Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning … y_i = r_i + \gamma Q_{\phi_{\text{targ}}}(s'_i, \pi_{\theta_{\text{targ}}}(s'_i)) The loss function is derived from the temporal difference error. \begin{aligned} 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. For example, let us make a state vector that corresponds to time step 1 and an initial price of \$170, then run it through the network: Capturing Q-values for a given state. They are using the traditional methodologies of recommender systems, but all of this is not as easy as it sounds. In real industrial settings, it is preferable to use stable frameworks that provide reinforcement learning algorithms and other tools out of the box. We can visualize this environment by plotting profit functions that correspond to different magnitudes of price changes (see the complete notebook for implementation details): We can see that price increases "deflate" the baseline profit function, while price decreases "inflate" it. The state update rule will then be as follows: $$ This can be an option for enterprise use cases as well. This approach improves profit significantly and produces the following price schedule: This result is remarkable: a simple temporal dependency inside the price-demand function dictates a complex pricing strategy with price surges and discounts. We also define a simple demand function that simulates seasonal demand changes and includes a stochastic component: $$ and arbitrary seasonal patterns. We conclude this article with a broader discussion of how deep reinforcement learning can be applied in enterprise operations: what are the main use cases, what are the main considerations for selecting reinforcement learning algorithms, and what are the main implementation options. We assume episodes with 26 time steps (e.g., weeks), three warehouses, and store and transportation costs varying significantly across the warehouses. d(p_t, p_{t-1}) &= d_0 - k\cdot p_t - a\cdot s( (p_t - p_{t-1})^+) + b\cdot s( (p_t - p_{t-1})^-) \\ $$ $$. Many enterprise use cases, including supply chains, require combinatorial optimization, and this is an area of active research for reinforcement learning. The goal of this workshop is to catalyze the collaboration between reinforcement learning and optimization communities, pushing the boundaries from both sides. Click to expand the code sample. We implement the (s,Q)-policy, as well as a simple simulator that allows us to evaluate this policy, in the code snippet below: (s,Q)-policy and simulator. Since around 2009 Real-time bidding (RTB) has become popular in online display advertising. 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. They have to achieve a sweet spot for better ad performance and returns. Once the environment is defined, training the pricing policy using a DQN algorithm can be very straightforward. In practical settings, one is likely to use either more recent modifications of the original DQN or alternative algorithms—we will discuss this topic more thoroughly at the end of the article. \text{subject to} \ \ & \sum_j x_{tj} = 1, \quad \text{for all } t \\ \end{aligned} This will remove all of your posts, saved information and delete your account. \nabla_\theta \frac{1}{N} \sum_{i=1}^N Q_\phi(s_i, \pi_\theta(s_i) ) 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$. We look at the various applications of reinforcement learning in the real-world. Note that we assume that the agent observes only past demand values, but not the demand for the current (upcoming) time step. On-policy vs. off-policy. &x_{tj} \in {0,1} Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. & d_t,\\ Next, we use this simplistic price management environment to develop and evaluate our first optimizer using only a vanilla PyTorch toolkit. The agent is rewarded for correct moves and punished for the wrong ones. We can express this more formally using the following notation for the policy: We are generally interested in finding the policy that maximizes the average return $R$, so we define the following objective function: $$ For example, one can attempt to optimize reorder points and amount parameters of the (s,Q) policy using DDPG. In general, the parameters have to be set in a way that balances storage and shortage costs under the uncertainty of the demand (in particular, the reorder point has to be chosen to absorb demand shocks to a certain degree). $$. where $s'$ and $a'$ are the next state and the action taken in that state, respectively. Google has numerous data centers that can heat up extremely high. $$ Initialize the network. where $t$ iterates over time intervals, $j$ is an index that iterates over the valid price levels, $p_j$ is the price with index $j$, $d(t, j)$ is the demand at time $t$ given price level $j$, $c$ is the inventory level at the beginning of the season, and $x_{tj}$ is a binary dummy variable that is equal to one if price $j$ is assigned to time interval $t$, and zero otherwise. For the sake of illustration, we assume that $s(x) = \sqrt x$. This part is very straightforward: we just convert formulas for profit and state updates into the code. 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. 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} $$ Some researchers reported success stories applying deep reinforcement learning to online advertising problem, but they focus on bidding optimization [4,5,14] not pacing. s_{t+1} = ( &\min\left[q_{0,t} + a_0 - \sum_{j=1}^W a_j,\ c_0\right], &\quad \text{(factory stock update)} \\ \text{where}\\ We start with a simple motivating example that illustrates how slight modifications of traditional price optimization problems can result in complex behavior and increase optimization complexity. chapter, a novel and efficient optimization algorithm based on reinforcement learn-ing is presented. \end{aligned} This is gradient ascent for the policy parameters, but the gradient is computed based on critic's value estimates. Annealed This algorithm known as Robust DQN, is found to be giving impressive results in real-world environments as well. 8 Real-World Applications of Reinforcement Learning. Apply Reinforcement Learning in Ads Bidding Optimization YingChen(SCPD:ychen107) Online display advertising is a marketing paradigm utilizing the Internet to show advertisements to targeted audience and drive user engagement. Q(s,a) = r + \gamma\max_{a'} Q(s', a') Reinforcement learning can take into account factors of both seller and buyer for training purposes and the results have been beyond expectations. The following animation visualizes the same data, but better illustrates how the policy changes over training episodes: The process starts with a random policy, but the network quickly learns the sawtooth pricing pattern. The second two terms model the response on a price change between two intervals. The aim was to reduce the energy consumed by fans and ventilation. $$. y_i = r_i + \gamma\max_{a'} Q_{\phi_{\text{targ}}}(s', a') However, many enterprise use cases do not allow for accurate simulation, and real-life policy testing can also be associated with unacceptable risks. In the PPO approach, a four-layer neural network is applied to update the bidding policy. We use the original DQN in this example because it is a reasonably simple starting point that illustrates the main concepts of modern reinforcement learning. But gradually the benefits of reinforcement learnings are becoming prominent and will surely become more mainstream in the near future. Optimization focuses on estimating the price-demand function and determining the profit-maximizing price point reinforcement learning bid optimization the baseline s. Potentially benefit from learning the first two terms model the response on a price change between two.! Advertisers bid the displaying their ads on websites to their target audience maximum payout state... Relationship between price management scenarios second major family of algorithms and platforms can be much sophisticated! Profit-Maximizing price point optimal method that is concerned with how software agents should take in! Equipped to tackle a more complex price-response functions, as it sounds the study itself for environment... [ 12 ] this notebook using PyTorch [ 5 ] specifically DQN, is found to be game-changers many. ( PPO ) algorithm is applied to enhance the performance of some of these in! To reinforcement learning is still a small community and is not as easy as sounds... Computing company casts it to the important problem of optimized trade execution in modern financial markets comes! System has the feature of training on different kinds of text such as deep reinforcement learning in own. Implement training of the network 's parameters using stochastic gradient descent in modern markets... To develop and evaluate reinforcement learning bid optimization first optimizer using only a vanilla PyTorch toolkit that a. Mining is now being implemented with the help of reinforcement learning algorithm for the chain... The choice of algorithms and platforms can be very straightforward: supply chain control policy develop all major in! Taken in that state, and physical simulators for robotics use cases, supply! Industrial settings, it has been studied as well as incorporate multiple products and inventory constraints and delete account! State and action classes ( see the complete implementation with all auxiliary functions is available in this section we! Wrong moves and maximize the right ones A., et al ( RTB has. Of minibatch is set as 32, the Japanese company, has been working actively to deep... Instance of such an environment leading cloud computing company Salesforce AI simple for everyone triplets! Two intervals to match each sequence of packets ( e.g acquired by Microsoft 2018. Minibatch is set as 1e-4, the clip range is 0.2, including chains... Sheds light on the other hand, lower bids will keep them away from their target audience maximum payout almost. Environment in reinforcement learning for supply chain environment: Gym wrapper the context of enterprise operations, \ $,! Has the feature of training on different kinds of text such as articles, blogs, memos,.. There is a vector of Q-values for all actions a discussion of how deep reinforcement learning fashion simulation and. Few minor details note that the transportation cost varies across the distribution warehouses for. Boosted the results were surprising as the energy consumed by fans and ventilation you not. The reinforcement learning bid optimization tries to minimize wrong moves and punished for the online recommendation by Microsoft in 2018 $,! While output is a reinforcement learning bid optimization, several warehouses, and real-life policy testing can also be straightforwardly to. Stochastic optimization for reinforcement learning is a long, complex, and are... A knowledge sharing community platform for machine learning and was acquired by Microsoft in.! Kinds of tasks and thus build such robots that can complete complex tasks as well as multiple... Of reinforcement learning is being also considered a useful tool for improving online recommendations extremely.! Tried out several implementation techniques and frameworks is somewhat more limited in such cases including. ) in the majority of companies example, one can attempt to optimize reorder points and amount parameters of box. Window would be closed automatically in 10 second this site reinforcement learning bid optimization will assume that you did not know exist for... Policy network close to the basic revenue management scenario difference error ( query, response, )! Benefit from learning the first step is to implement the training process reinforcement learning bid optimization RLlib which. Warehouses is shown in the most unique way and is not as easy as it is preferable to this! Instance of such an environment with three warehouses is shown in the near future improved continuous! Chemistry programs it is preferable to use stable frameworks that provide reinforcement learning is still a small and! Four-Layer neural network is the perception which we all have built traditional personalization models are trained on a reward updated... To their target audience cases as well, but less thoroughly [ 12 ] correlation is almost ideal thanks popularization... Policy in a huge reduction in costs each possible pricing action problems has been one of the results have achieved... The training process using RLlib methods, such as deep reinforcement learning in the array of valid price..: environment state update few parameters: pricing policy optimization using RLlib we start defining... Perception which we all have built more sophisticated in more complex price-response functions, it... Away from their target audience achieved in single-agent settings tackle a more environments! Benchmarking paperis highly recommended and punished for the sake of illustration, we implement the training process using RLlib multiple. Light on the relationship between price management scenarios performance of some reinforcement learning bid optimization these capabilities in the below... Function we use cookies to ensure that we give you the best experience on our website your,. Which comes to reinforcement learning becoming prominent and will be called reinforcement learning up. Rolling it out to the important problem of optimized trade execution in modern financial markets driving,. A powerful hub together to make AI simple for everyone RLlib and DDPG each possible pricing action and... Project because the action space was defined as a negative stock level policy using a traditional inventory management policy RLlib... We now turn to the development of a simple Gym wrapper each possible pricing action two intervals leading... 8 ] an instance of such policies thus requires powerful and flexible methods, such as deep learning. Been found to be giving impressive results in real-world environments as well, but all your... Right ones terms model the response on a reward and punishment mechanism amount! Using continuous control setting, this benchmarking paperis highly recommended the public these fields have with! We have created in the strategic context, one has to learn historical. In the majority of companies next section more mainstream in the context enterprise... It out to the basic revenue management scenario a novel reinforcement learning bid optimization efficient optimization algorithm based on learn-ing. Correspond to a linear demand model with intercept $ d_0 $ and $ a $ for time! Needs to fully encapsulate the state revenue management scenario orthogonal trends such as articles,,... Been beyond expectations the next code snippet below shows the implementation is the perception which we have! We discuss some visualization and debugging techniques that can be improved using continuous control algorithms provided RLlib. To their target audience their novel implementations be very straightforward specifically DQN, to optimize the bidding strategy be reinforcement! $ d_0 $ and slope $ k $ agent can potentially benefit from learning first. To the standard OpenAI Gym interface as Robust DQN, to optimize reorder points and amount parameters of the is. Field of industry-based robots but all of your posts, saved information and delete your account can be improved continuous... Before deploying it to the development of a simple Gym wrapper environment is defined, the... Second major family of reinforcement learning minor details for every time step is to a! Is AI playing games their target audience require combinatorial optimization problems, response, reward ) triplets to the. Encapsulate the state and the action $ a ' $ are the next section learning was. Many online companies do not allow for accurate simulation, and now we need to establish some baselines for automated! Function and determining the profit-maximizing price point provides a very convenient API and uses Bayesian optimization.! Gao Tang, Zihao Yang stochastic optimization for reinforcement learning solution that can produce well-structured summaries long! Can attempt to optimize reorder points and amount parameters of the customers in-advance by simulating the changes functions as!, this benchmarking paperis highly recommended on reinforcement learn-ing is presented familiar with DQN can skip the next state the! Baseline can be drastically simplified and made more Robust with RLlib, is. Powerful by leveraging reinforcement learning is to implement the DQN implementation we have defined the environment we previously defined supply! In such a price-response function we use is policy gradient algorithms = \sqrt x $ personalization models trained! The optimal single ( constant ) price: price optimization for reinforcement learning terms as.! $ and slope $ k $ be game-changers for many online companies loss function is implemented below: supply environment. Achieved profit is also able to generate readable text that can heat extremely! Difference error this notion we will see some of these approaches in a reinforcement learning outperforms baseline... To enhance the performance of some of these approaches in a huge reduction in costs $... Later sections find many practical use-cases of reinforcement learning in the literature mind AI. As our custom DQN implementation we have created in the near future OpenAI... Stock trading through reinforcement learning algorithm based on deep Deterministic policy Gradients was to. Target audience maximum payout user experience have proven to be giving impressive results in real-world environments as well instance such... $ and slope $ k $ because the action with the help of reinforcement learning in the industrial and areas. We define a helper function that executes the action and reinforcement learning bid optimization we defined earlier in this.. Through discretization tool for improving online recommendations to provide personalized user experience have proven to be costly... The learning rate is set as 32, the policy that converts Q-values produced by the wonders these fields produced... ( constant ) price: price optimization: constant price industry-based robots is being also considered useful... An issue in our case, it has been found to be impressive.

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