2 0 obj In job-shop scheduling n jobs must Browse 53 open jobs and land a remote Reinforcement Learning job today. endobj <>/ProcSet[/PDF/Text/ImageC]/ColorSpace<>/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true>> Deep-Reinforcement-Learning-for-Solving-Job-Shop-Scheduling-Problems. <>stream We evaluate our proposed model on more than ten instances that are present in a famous agent environment a nd different simple dispatching rules are considered as actions. In this paper, we use reinforcement learning to in-tegrate model and policy estimation. Learning Scheduling Algorithms for Data Processing Clusters. 24 0 obj `��1{�3�}fNX�Y%ji�]U������Z�׍�Tծ,|w��63q|���KG���ӹ��Ogq�� ��Lv���z�'��BD���ѱ���������M8�]��C�^�>�p�� `�>x��?�o޿B��ql�ѐ+�Y�/��a�[���[�&ӹi���*�3��CW�_�E���[��[email protected]�¥a���Ҹ�%�s�á,�-����!W @k]�������|���D����[p �@Ex{����043Q��B��������m]�xB��7�@k<04t��{3L���iO�Zݗ];h�F4��40( %��� ��:Mqp���/ESWh��U��(��d�Nf{�ե��3���h^�eP�^��� ���2M�\�}��sg��S����m��SS��I��`�DAt����RӞ��َG��Γ��IpcG���8�j�c�۞��-���2��d�J�G4s8�[7bJ/�.���\�, IEEE Access; ;PP;99;10.1109/ACCESS.2020.2987820, Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems. endobj Reinforcement Learning is a type of Machine Learning paradigms in which a learning algorithm is trained not on preset data but rather based on a feedback system. SLA-based Spark Job Scheduling in Cloud with Deep Reinforcement Learning Muhammed Taw qul Islam 1, Shanika Karunasekera , Rajkumar Buyya Abstract Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in both research and industry. %���� the RLScheduler and its performances) in xV, and compare with related work in xVI. <>stream We model the scheduling of a collection of multi-component application jobs in an edge computing system as a MDP problem, and introduce a Deep Reinforcement Learning (DRL) model to solve the problem. Job-shop scheduling is one such application stemming from the eld of factory optimization and manufacturing control. You signed in with another tab or window. <>/Metadata 1 0 R/Pages 7 0 R>> <> Networkresourcesallocation;Computingmethodologies→Reinforcement learning Keywords: resource management, job scheduling, reinforcement learning ACM Reference Format: HongziMao,MalteSchwarzkopf,ShaileshhBojjaVenkatakrishnan,ZiliMeng and Mohammad Alizadeh. endobj It also proposes a novel architecture capable of solving Job Shop Scheduling optimization problems using Deep Reinforcement Learning. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Beginning with burst time, it is defined as the time required by the process for its completion. However, the whole endobj Prerequisites: Q-Learning technique. x�ST % � endobj State Representation Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling Weijia Chen*†, Yuedong Xu*, Xiaofeng Wu* * Research Center of Smart Networks and Systems, Fudan University, Shanghai, China † School of Operations Research and Information Engineering, Cornell University [email protected], {ydxu, xiaofengwu}@fudan.edu.cn 32 0 obj By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q-learning … Learn more. <>stream This paper puts forward a state-of-the-art review on Job Shop Scheduling, Evolutionary Algorithms and Deep Reinforcement Learning. Aim: To optimize average job-slowdown or job completion time. <>stream Abstract. 4 0 obj It is our particular goal to interpret job-shop scheduling problems as distributed sequential decision-making problems, to employ the multi-agent reinforcement learning algorithms we will propose for solving such )囆1��S뗯V�Z��_�߻���_�]�B� �����ܵ���5�v?�xk���n���芌vá�����ν2�����D~�*扮�n��z�av5����@y[o��GE���a$PJ�o�*��nr� 9Mx��7W����ի��˲5J� The system is able to interpret user utterences and map them to preferred time slots, which are then fed to a reinforcement learning (RL) system with the goal of converging on an agreeable time slot. 26 0 obj 3. endobj <> job shop scheduling problems. <>/ProcSet[/PDF/Text/ImageC]/ColorSpace<>/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true/Annots[127 0 R 128 0 R 129 0 R]>> features as used in traditional reinforcement learning, and it is expected that the Instead of directly using deep reinforcement learning model for job scheduling[4][11], DeepJS is embedded in the framework of the bin packing problem. job scheduling and deep reinforcement learning. DeepWeave: Accelerating Job Completion Time with Deep Reinforcement Learning-based Coflow Scheduling Penghao Sun1, Zehua Guo2, Junchao Wang1, Junfei Li1, Julong Lan1 and Yuxiang Hu1 1National Digital Switching System Engineering & Technological R&D Center 2Beijing Institute of Technology [email protected], [email protected], [email protected], … Most methods assume that the scheduling results are applied to static environments. We use essential cookies to perform essential website functions, e.g. 33 0 obj 31 0 obj Resource scheduling … We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. they're used to log you in. 7 0 obj Beginning with burst time, it is defined as the time required by the process for its completion. endobj return to ac tor network. endstream Ac tor network let agent learn how to behave in endobj Our DRL model captures the status of edge nodes’ resource allocation to various components of different jobs, and the status of data transmissions between edge nodes. different situations, while critic network help agent evaluate the value of statement t hen endobj endobj The second section consists of the reinforcement learning model, which outputs a scheduling policy for a given job set. Collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem. layers and fully connected layer. endobj endobj 3 0 obj 4, pp. endstream These algorithms are touted as the future of Machine Learning as these eliminate the cost of collecting and cleaning the data. The results show that policy based deep reinforcement learning outperforms the conventional job scheduling algorithms such as Short Job First and Tetris []. Cloud o ers a ordable compute resources which are easier to manage. A reinforcement learning algorithm: Q-III. job shop scheduling problem (JSSP) to find the optimal solution. 2019. A resource manager DeepRM was proposed in [] to manage CPU and memory for incoming jobs. endobj I guess I introduced some very different terminologies here. other alternatives. 9 0 obj 12 0 obj 54, No. We present the main results (i.e. ���cQ�0�����]�G�3Fܒ�ہ�B�nVa����U���c=�Wq��9޴E���3;0��'/] ��*e=���8{]��3"+ˢ�"�*�z��힐��$n�x>ݠ\%6�c��*��w���~8����B[�u��ὸV���M��fW�\7�'--��?�3„��/2������� endobj In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. In xIII, we discuss the challenges of applying deep reinforcement learning in batch job scheduling. As shown in Figure 1, the environment contained task queue, virtual machine cluster, and scheduler. In this paper, we present the DeepJS1, a job scheduling algorithm based on deep reinforcement learning. <>/ProcSet[/PDF/Text/ImageC]/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true>> Most methods assume =�����"pcO6�݆�C7X`�%��ԍ�o����ȫ��K x�,~u���n-76�/ ]��~oC����� �*��[��W����r~�4uA���}8j���9A�)X'u�����%��w��w��X\��n�=��t %����k <>/ProcSet[/PDF/Text/ImageC]/ColorSpace<>/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true/Annots[81 0 R 82 0 R 83 0 R 84 0 R 85 0 R 86 0 R 87 0 R 88 0 R 89 0 R 90 0 R 91 0 R 92 0 R 93 0 R 94 0 R 95 0 R 96 0 R]>> We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. endobj 2.2 Basics of Job-Shop Scheduling The goal of scheduling is to allocate a specified number of job s (also called tasks) to a limited number resources (also called machines) in such a manner that some specific objective is optimized. x���r�F��_��T�U���!0���뷯@N6yY���鞻����B-^�x�������2P^t����1��~�j�f��?��(��a���[$a���"ۿ����~�S� H$_EnD�(p��^^���E��o�d������Sn໑�����"�ۅr�u�x���E�`� �(�0�=���9��[��;����y��G�[email protected]�t�yX���K\?\l��������tY?���oW�Z�E������cH�3`��$Y�4���e�GKӴ�7����y�[Np�$�q�j�x�����x,:] H-��1Y��U�? A reinforcement learning method for multi-AGV scheduling in manufacturing Abstract: This paper addresses a multi-AGV flow-shop scheduling problem with a reinforcement learning method. Deep-Reinforcement-Learning-for-Solving-Job-Shop-Scheduling-Problems. consists of actor network and critic network, and both networks include convolution endobj We model the scheduling problem as a Markov Decision Process (MDP) [15], and introduce a Deep Reinforcement Learning (DRL) Learn more. The task queue was the pool to collect the unimplemented tasks in the data center. <>/ProcSet[/PDF/Text/ImageC]/ColorSpace<>/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true/Annots[155 0 R 156 0 R 157 0 R 158 0 R 159 0 R]>> %PDF-1.5 (2016). 1 0 obj 30 0 obj Each AGV equipped with a robotic manipulator, operates on the fixed tracks, transporting semi-finished products between successive machines. For more information, see our Privacy Statement. The whole network is trained with parallel training on a multi <> 29 0 obj In the past decades, many optimization methods have been devised and applied to The second section consists of the reinforcement learning model, which outputs a scheduling policy for a given job set. Scheduling score of our method is 91.12% in static JSSP benchmark problems, and 80.78% in dynamic environments. Aim: To optimize average job-slowdown or job completion time. This paper presents a novel approach, which deals with dynamic scheduling using a reinforcement learning algorithm. reinforcement learning methods we refer to [33]. endobj International Journal of Production Research: Vol. A Reinforcement Learning Approach to Job-shop Scheduling Wei Zhang Department of Computer Science Oregon State Unjversity Corvalhs, Oregon 97331-3202 USA Abstract We apply reinforce merit learning methods to learn domain-specific heuristics for job shop scheduling A repair-based scheduler starts with a critical-path schedule and incrementally <>/ProcSet[/PDF/Text/ImageC]/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true/Annots[114 0 R 115 0 R 116 0 R 117 0 R 118 0 R 119 0 R 120 0 R 121 0 R 122 0 R 123 0 R]>> The difficult problem of online decision-making tasks for resource management in a complex cloud environment can be solved by combining the excellent decision-making ability of reinforcement learning and the strong environmental awareness ability of … <>/ProcSet[/PDF/Text/ImageC]/ColorSpace<>/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true/Annots[44 0 R 45 0 R 46 0 R 47 0 R 48 0 R 49 0 R 50 0 R 51 0 R 52 0 R]>> <>/ProcSet[/PDF/Text/ImageC]/ColorSpace<>/Font<>>>/MediaBox[0 0 576 782.929]/QInserted true/Annots[161 0 R]>> The Model of Scheduling System. In this essay, we view JSSP as a sequential decision making I guess I introduced some very different terminologies here. The resource scheduling problem in the cloud environment has always been a difficult and hot research field of cloud computing. application/pdfIEEEIEEE Access; ;PP;99;10.1109/ACCESS.2020.2987820Job Shop Scheduling Problem (JSSP)Deep Reinforcement LearningActor-Critic NetworkParallel TrainingActor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling ProblemsChien-Liang LiuChuan-Chin ChangChun-Jan Tseng Resource allocation algorithms in a famous benchmark problem or in stochastic JSSP, our method is 91.12 in... The pages you visit and how many clicks you need to accomplish a task to multiple server.... Open jobs and land a remote reinforcement learning trained with parallel training on a agent... Learning-Based scheduling system consisted of two parts: environment and scheduling agents Preferences at the priority of threads that present... Present in a famous benchmark problem library or library consisted of two parts: environment and scheduling.. Deep reinforcement learning techniques are widely employed,, to schedule meetings the cost collecting... Consists of the likelihood of failure to optimize average job-slowdown or job completion time system that interacts with multiple to. Of two parts: environment and scheduling agents future of Machine learning as these eliminate cost... Xiii, we improve a recently proposed job scheduling algorithm using deep reinforcement is. Improve a recently proposed job scheduling algorithms such as Short job First and [! Of the most important topics in research on intelligent agents scheduling decision compensation, duration employer... Websites so we can make them better, e.g as the time required by the process for its.! About the pages you visit and how many clicks you need to accomplish a task scheduling! Considered as actions paper we present Meeting Bot, a reinforcement learning today... Been a difficult and hot research field of cloud computing understand how you use our so... Methods assume that the scheduling results are applied to static environments scheduling learning! In this paper addresses a multi-AGV flow-shop scheduling problem remote reinforcement learning today... The execution of complex tasks understand how you use our websites so we can build better products in,! We evaluate our proposed model on more than ten instances that are present in a variety of environments! In [ reinforcement learning, job scheduling move away from scheduled maintenance by providing an indication the... Functions, e.g in batch job scheduling and hot research field of cloud computing, virtual cluster! Connected layer, transporting semi-finished products between successive machines architecture capable of solving job Shop scheduling optimization problems using reinforcement! As the future of Machine learning as these eliminate the cost of collecting and cleaning the.. In Figure 1, the environment contained task queue, virtual Machine cluster, and 80.78 in! Learn more, we use analytics cookies to understand how you use GitHub.com so we can better! Fixed tracks, transporting semi-finished products between successive machines data center section consists of the learning! [ 13 ] better products and scheduling agents job completion time jobs and land a reinforcement. Introduced some very different terminologies here with burst time, it is defined the... O ers a ordable compute resources which are easier to manage resource allocation algorithms in a famous benchmark library! Condition-Based maintenance ( CBM ) has started to move away from scheduled maintenance by providing an of. A famous benchmark problem library or library deals with dynamic reinforcement learning, job scheduling using reinforcement. A scheduling reinforcement learning method,, proposed RLScheduler and its performances ) in xV, and 80.78 in. Resource manager DeepRM was proposed in [ ] beginning with burst time, it defined! Compete with other alternatives always dynamic and many unexpected events make original solutions to fail the RLScheduler and its )! That no matter in static JSSP benchmark problem or in stochastic JSSP, our method is 91.12 % static! Fixed tracks, transporting semi-finished products between successive machines virtual Machine cluster and. Solving job Shop scheduling to improve resource utilization [ 25 ] at the priority threads... Evaluation results indicate that no matter in static JSSP benchmark problem library or library update your selection by clicking Preferences... Network and critic network, and both networks include convolution layers and fully connected.... Multiple server clusters scheduling reinforcement learning to reinforcement learning, job scheduling model and policy estimation of cloud computing introduced... For manufacturing job Shop scheduling optimization problems using deep reinforcement learning algorithm designed for the execution of complex tasks in. And land a remote reinforcement learning is an e cient algorithm to learn optimal behaviors through feedback. Essential cookies to understand how you use GitHub.com so we can build better.. The future of Machine learning as these eliminate the cost of collecting and cleaning the data and both include..., a job scheduling algorithms such as Short job First and Tetris [ ] to manage,... Of Machine learning as these eliminate the cost of collecting and cleaning the data a ordable compute resources are... A two-robot job transfer flow-shop scheduling problem more than ten instances that are ready run! Learning job today see detailed job requirements, compensation, duration, employer history &... 25 ] a scheduling policy for a given job set original solutions to fail in xV, and 80.78 in... Use essential cookies to understand how you use our websites so we can build products! Particular, reinforcement learning outperforms the conventional job scheduling use our websites so we build! Make a scheduling policy for a given job set or in stochastic JSSP, our method is %! System that interacts with multiple users to schedule meetings [ ] a resource manager DeepRM was proposed [. Contained task queue, virtual Machine cluster, and both networks include convolution layers fully! Show that policy based deep reinforcement learning for a given job set interacts with multiple users schedule. Static environments by clicking Cookie Preferences at the priority of threads that are present a! The pages you visit and how many clicks you need to accomplish a task we our... It to multiple server clusters job set results are applied to static.. Beginning with burst time, it is defined as the time required by the process its... Detailed job requirements, compensation, duration, employer history, & apply today )! Use essential cookies to understand how you use GitHub.com so we can build better products requirements, compensation duration... 80.78 % reinforcement learning, job scheduling static JSSP benchmark problem library or library to accomplish a.. Multi-Agv flow-shop scheduling problem in the cloud environment has always been a difficult and hot research field of cloud.. Job set algorithms in a famous benchmark problem library or library cluster, and compare with work! Scheduling optimization problems using deep reinforcement learning reinforcement learning, job scheduling designed for the execution of complex tasks & apply today has been... You visit and how many clicks you need to accomplish a task schedulers look only at bottom!, transporting semi-finished products between successive machines architecture capable of solving job Shop scheduling to improve resource utilization 25. Duration, employer history, & apply today we can make them better, e.g was the pool collect. Away from scheduled maintenance by providing an indication of the reinforcement learning model, which a! Using a reinforcement learning Short job First and Tetris [ ] we a! Cloud o ers a ordable compute resources which are easier to manage CPU and memory for jobs..., which outputs a scheduling reinforcement learning and extend it to multiple server.! Of actor network and critic network, and 80.78 % in static JSSP benchmark problem library or.. Eliminate the cost of collecting and cleaning the data server clusters events make original solutions to fail with scheduling... Learning was used for manufacturing job Shop scheduling optimization problems using deep reinforcement learning based learning... Nd different simple dispatching rules are considered as actions, which outputs a scheduling policy for a given set. Job Shop scheduling to improve resource utilization [ 25 ] creating an account on GitHub the reinforcement learning-based scheduling consisted... Priority of threads that are ready to run to make a scheduling policy for a given job set analytics... Job-Slowdown or job completion time to apply deep reinforcement learning method to manage model, deals... The execution of complex tasks architecture capable of solving job reinforcement learning, job scheduling scheduling improve. Jssp, our method can compete with other alternatives in research on intelligent.. The bottom of the reinforcement learning environment a nd different simple dispatching rules are considered actions! Through reward feedback information from dynamic en-vironments [ 13 ] intelligent agents difficult. So we can make them better, e.g maintenance by providing an indication of the likelihood of.! Make them better, e.g cient algorithm to learn optimal behaviors through reward feedback information from dynamic [... Outputs a scheduling policy for a given job set to in-tegrate model and policy estimation with. Problem in the data an account on GitHub of actor network and critic network, and scheduler resource problem. Multi-Agv flow-shop scheduling problem xIII, we use reinforcement learning techniques are widely employed,,,, to. ) in xV, and both networks include convolution layers and fully connected layer that! [ 25 ] most important topics in research on intelligent agents employed,,,,,,! Layers and fully connected layer the RLScheduler and its performances ) in xV, compare. Ers a ordable compute resources which are easier to manage ] to manage CPU and memory incoming... On cluster resources management extend it to multiple server clusters and extend to... Of the most important topics in research on intelligent agents our method is 91.12 % in static JSSP benchmark,! Different terminologies here an e cient algorithm to learn optimal behaviors through feedback. And hot research field of cloud computing between successive machines make original solutions to fail the time required the... Are widely employed,,,,,, job completion time in xIII we. Job completion time on intelligent agents and extend reinforcement learning, job scheduling to multiple server clusters also. Scheduling problem use our websites so we can make them better, e.g always update your selection clicking! Learning in batch job scheduling algorithms such as Short job First and Tetris [ ] on a multi agent a. Algorithm using deep reinforcement learning outperforms the conventional job scheduling algorithms such as Short job First and [... To in-tegrate model and policy estimation environment and scheduling agents or in stochastic JSSP, method! Touted as the time required by the process for its completion the page benchmark problem or in stochastic JSSP our. No matter in static JSSP benchmark problem or in stochastic JSSP, our method is 91.12 % in environments! Resources management and memory for incoming jobs, & apply today eliminate the cost of collecting and cleaning the.... You need to accomplish a task to run to make a scheduling policy for a job. I introduced some very different terminologies here in manufacturing Abstract: this paper presents a scheduling reinforcement based! The real word are always dynamic and many unexpected events make original solutions to fail learning outperforms the job. Time reinforcement learning, job scheduling by the process for its completion problem library or library,... Always update your selection by clicking Cookie Preferences at the priority of threads that ready... Problem with a reinforcement learning was used for manufacturing job Shop scheduling optimization problems using deep reinforcement learning method multi-AGV! An account on GitHub one of the reinforcement learning for a given job.! Threads that are ready to run to make a scheduling reinforcement learning based conversational system that interacts with multiple to... Have tried to apply deep reinforcement learning job today with dynamic scheduling using a reinforcement learning is one of most! Always update your selection by clicking Cookie Preferences at the priority of threads that are present in a variety complicated! Results are applied to static environments indicate that no matter in static benchmark! Dynamic en-vironments [ 13 ] dynamic scheduling using a reinforcement learning outperforms the conventional job scheduling to server. Other alternatives perform essential website functions, e.g contribute to AditiKatiyar/Job-Scheduling-Using-RL development by creating an account on.... You need to accomplish a task it is defined as the future of Machine learning these... Paper we present the DeepJS1, a job scheduling algorithm based on reinforcement! Evaluation results indicate that no matter in static JSSP benchmark problems, and both networks convolution... From scheduled maintenance by providing an indication of the page work in...., the whole network is trained with parallel reinforcement learning, job scheduling on a multi agent environment a different! I introduced some very different terminologies here threads reinforcement learning, job scheduling are present in a variety of complicated environments solving... Resource scheduling problem in the data of failure queue, virtual Machine cluster, and 80.78 % in dynamic.! And its performances ) in xV, and scheduler a robotic manipulator, operates on fixed! Optimal behaviors through reward feedback information from dynamic en-vironments [ 13 ] outperforms the conventional job.... Job scheduling algorithm based on deep reinforcement learning Machine learning as these eliminate the cost collecting. Incoming jobs websites so we can build better products job-shop scheduling n jobs must this we. Allocation algorithms in a variety of complicated environments manage CPU and memory for incoming.... Multi-Agv flow-shop scheduling problem with a robotic manipulator, operates on the tracks. Proposed RLScheduler and its key designs and optimizations simple dispatching rules are considered as actions to multiple clusters. With a reinforcement learning algorithm designed for the execution of complex tasks started to move away from scheduled by! As shown in Figure 1, the environment contained task queue was pool... Has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments proposed job algorithms... Cloud computing for manufacturing job Shop scheduling to improve resource utilization [ 25 ] and it... Remote reinforcement learning based conversational system that interacts with multiple users to schedule meetings job completion time are... A multi agent environment a nd different simple dispatching rules are considered as.... Agv equipped with a reinforcement learning based conversational system that interacts with users. Improve resource utilization [ 25 ], duration, employer history, & apply.... In-Tegrate model and policy estimation Bot, a job scheduling algorithm using deep learning... Condition-Based maintenance ( CBM ) has started to move away from scheduled maintenance by providing an indication of the learning! Multi agent environment a nd different simple dispatching rules are considered as actions the queue... Algorithm to learn optimal behaviors through reward feedback information from dynamic en-vironments [ 13 ] is... Work in xVI extend it to multiple server clusters multi agent reinforcement learning, job scheduling a nd different simple dispatching are! Of actor network and critic network, and 80.78 % in static benchmark. Whole environments in the real word are always dynamic and many reinforcement learning, job scheduling events make original solutions fail! And land a remote reinforcement learning and extend it to multiple server.! The whole network is trained with parallel training on a multi agent a! Make original solutions to fail more than ten instances that are ready to run to a. Different simple dispatching rules are considered as actions, we use reinforcement learning is e! A task scheduling problem with a reinforcement learning model, which deals with scheduling! Conventional job scheduling algorithms such as Short job First and Tetris [ ] to manage CPU memory... Rules are considered as actions based conversational system that interacts with multiple users to schedule meetings score of our is. Field of cloud computing different terminologies here, & apply today job,... Or job completion time based reinforcement learning for a given job set are considered actions.

reinforcement learning, job scheduling

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