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    Oct 27, 2020 · Deep Multi-Agent Reinforcement Learning for Dynamic and Stochastic Vehicle Routing Problems. Routing issues are considered in the CRAHN 2 Multi-Agent Reinforcement Learning for Network Routing 1 Consider the network system as a whole agent and update each router through distributed optimization. August 2018. It allows a single agent to learn a policy that maximizes a possibly delayed reward signal in a stochastic Sequential transfer in reinforcement learning with a generative model Authors: Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli. Therefore, the reinforcement learning methodology is a good potential underlying methodology to Reinforcement learning (RL) [ 29] allows for an alternate approach in which an RL agent trained on a set of target network conditions learns to make routing decisions even in an uncertain and time-varying environment. To make it more practical, a demo is provided to show and compare different model We pro- pose a solution approach that combines Deep Reinforce- ment Learning (specifically neural networks-based Temporal-. Using reinforcement learning for routing is not a new idea. Machine learning is assumed to be either supervised or unsupervised but a recent new-comer broke the status-quo - reinforcement learning. 29 Apr 2019 Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches. Sep 21, 2020 · An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning Murtadha M. Tabular implementations of reinforcement learning methods are the most simple, though, they suffer from the curse of dimensionality problem; therefore, function approximation techniques have been used to provide We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules Reinforcement Learning for Routing and Spectrum Management in Cognitive Wireless Mesh Network: 10. : Conf. in 2002 [ 1 J. University  In this paper, we formalize the customer routing problem, and propose a novel framework based on deep reinforcement learning. The agent observes state and reward from the operating environment and takes the action& Feature Engineering for Deep Reinforcement. Phys. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. * Universitat Politecnica de Catalunya. January 4, 2021 in Open Spring 2021, Open Summer 2021, Open Fall 2021. The Internet is a complex collection of inter-connected networks with a numerous of inter-operable technologies and protocols. In this approach, we train a single policy model that finds near-optimal solutions for a broad range of problem instances of similar size, only by observing the reward signals and following feasibility rules. YEUNG on reinforcement learning (RL), called Q-   26 Aug 2020 Research on deep reinforcement learning multi-path routing planning in. Shortest path routing is most suitable network routing algorithm for wired network but not suitable f 27 Feb 2020 The results show that by training with a broad distribution of loads, it is possible to get a model, capable of routing in highly congested baggage handling systems. Learning Based Routing. Jobb. Department of Automatic Control, Lund University. 2. Reinforcement learning is a field in machine learning concerned with programs taking optimal action sequences so as to achieve a goal. Littman, J. Reinforcement Learning for Solving the Vehicle Routing Problem Mohammadreza Nazari Afshin Oroojlooy Martin Takác Lawrence V. Vehicle Routing Problem (VRP) using deep re- inforcement learning. Alkadhmi , Osman N. Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning Tung-Che Liang 1Zhanwei Zhong Yaas Bigdeli Tsung-Yi Ho2 Krishnendu Chakrabarty 1Richard Fair Abstract We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance. The new framework successfully resolves problems with prior design approaches, which are either unreliable due to random searches or inflexible due to severe design space restrictions. The breakthrough of deep reinforcement learning (DRL) provides a new opportunity to a good many RL- based net-. actions are to be learned with a desired outcome. Vehicle routing has been extensively studied in optimization problems. We leave the reader with many Reinforcement learning based multipath routing . Dec 22, 2020 · This paper proposes a novel and scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. 9 May 2019 Reinforcement learning (RL) has been introduced to design autonomous packet routing policies with local information of stochastic packet  With a small case study on a mid-sized network, we demonstrate the significant advantages of using Reinforcement Learning to solve for the optimal routing  Q-Routing: Q-Routing is an adaptive routing algorithm that was proposed by Boyan and Littman in. Abstract: We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles Motivated by the promising advances of deep-reinforcement learning (DRL) 12/06/2019 ∙ by Jose Manuel Vera, et al. Using Deep Reinforcement Learning. , the half-perimeter wirelength, HPWL) and approximate congestion (the fraction of routing resources consumed by the placed netlist). Deep-RMSA: A Deep-Reinforcement-Learning Routing,. This pa- per proposes a novel deep reinforcement framework, tak- ing routerless networks-on-chip (NoC) as an evaluation case study. Freelancer. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. Snehal Sudhir Chitnavis. INDEX TERMS Reinforcement learning, Communication networks, Routing protocols, Path optimization,. Abstract. Bi-objective school bus scheduling optimization problem that is a subset of vehicle fleet scheduling problem is focused in this paper. (RL) to address this problem. Reinforcement learning gives us a technique to program routers with reward and punishment, without the necessity to specify how an agent should be reached. We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. Through the comparisons of simulated results Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Abstract: This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). Routing delivery vehicles in dynamic and uncertain environments like dense city centers is a challenging task, which requires robustness and flexibility. I. 26 Feb 2019 Reinforcement-learning-based routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search for the optimal  7 Feb 2019 M. We argue Imitation learning can be used to “bootstrap” reinforcement learning by providing a non-random set of actions to try at first, learned from watching humans. 3 Routing with reinforcement learning. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing A Distributed Reinforcement Learning Scheme for Network Routing book By M. Pere Barlet- Ros* and Albert Cabellos-Aparicio*. Then select the opt This paper describes an application of reinforcement learning to the problem of routing in networks where each edge can be represented by a very conservative upper bound on the delay to traverse it, but the typical delay experienced when traversing edges can differ from its upper bound. This is an UNPAID research project. Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection Online Opportunistic Routing in Cognitive Radio Ad-Hoc Network and Spectrum Management Reinforcement learning is a computational approach to learning from interaction. edu Abstract We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. 7 Jan 2020 In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintain We study the feasibility of implementing an adaptive routing policy using the Q- Learning algorithm which learns sequences of actions from delayed rewards. Computer networks and reinforcement learning algorithms have substantially advanced over the past decade. Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks 1. Reinforcement Learning (DQN) Tutorial¶ Author: Adam Paszke. See full list on medium. José Suárez-Varela, Albert Mestres, Junlin Yu, Li Kuang, Haoyu Feng, Albert Cabellos-Aparicio, Pere Barlet-Ros; "Routing in Optical Transport Networks with Deep Reinforcement Learning," in Journal of Optical Communications and Networking, vol. P. Ali !,# , Bilgehan Erman #, Ejder Baştuğ # and Bruce Cilli # !Penn State, # IP Networking, ENSA Lab, Nokia Bell Labs. In their paper “Attention! Learn to Solve Routing Problems”, the authors tackle several combinatorial optimization problems that involve routing agents on graphs, including our now familiar Traveling Salesman Problem. Routing enables a source node to search for a least-cost route to its destination node. Jan 04, 2021 · Reinforcement learning for vehicle routing. The astonishing accuracy with which Moore's law [28] has been adhered to all In this paper, optimized reinforcement learning-based adaptive network routing is investigated. We ˝rst illustrate the challenges of the existing routing protocols when the amount of the data explodes. J. Ser. Following, the in routing. Snyderˇ Department of Industrial and Systems Engineering Lehigh University, Bethlehem, PA 18015 {mon314,afo214,takac,lvs2}@lehigh. The advantages of using RL as route optimization is the automatic adaption to the environment it is used on. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Related Work. Abstract and Figures In this paper we describe a self-adjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. A DMFB, composed of a two-dimensional electrode array, manipulates discrete fluid droplets to automatically execute biochemical protocols such as point-of-care clinical diagnosis. Q-learning - Wikipedia. Routing, Reinforcement Learning (RL), Two-opt algo- rithm, Support Vector Machines (SVMs). Our approach significantly differs from existing reinforcement learning algorithms for vehicle routing problems, and allows us to obtain comparable results with much simpler neural architectures. These two algo-rithms are fully adaptive to topology changes and changes in link costs in the network, and have space and computational overheads that are competitive with traditional packet routing algorithms: although they can generate more routing traffic when the rate of failures in a optimization problems in general and the Capacitated Vehicle Routing Problem in particular. Outlining a research agenda for data-driven routing. Abstract : In VLSI design, routing is the step that determines the paths for  17 Oct 2016 SDN has simplified routing by employing a central controller that collocates all routing decisions and communicating the forwarding decisions  Multi-Agent Deep Reinforcement Learning: Multi-agent systems can be naturally used to model many real world problems, such as network packet routing and  19 Jan 2017 How reinforcement learning is used in Artificial Intelligence, machine learning and deep learning. This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Contribute to softmicro929/q-routing development by creating an account on GitHub. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. Multi‐metric considered for routing is the following: node delay, node distance, node stability, node mobility, and node degree. Through the combination of Reinforcement learning and neural network, which means the Q-table in Q-learning is replaced by neural network, we present routing algorithm based on Deep Q-learning. We present three RL-based methods for learning Computer Systems where reinforcement learning can be used to optimize network routing which is the main theme of this article. 4. 4018/IJWNBT. The proposed algorithm makes routing decisions by holistically considering the energy consumption of the network. ing to this problem. Figure 4: Applications of Reinforcement Learning a q-learning algorithms on packet routing. S. Using Q-value algorithm find the reward and it is updated. ∙ 0 ∙ share Vehicle routing has been extensively studied in optimization problems. In this approach, we train a single policy model that finds near-optimal solutions for a broad range of problem instances of. Apr 22, 2020 · In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Abstract: We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved Deep reinforcement learning (DRL) has recently revolutionized the resolution of decision-making and automated control problems. Reinforcement learning is well-suited for adaptive routing because (a) it is goal-oriented, i. In the field of reinforcement learning, we detect two major strategies. S. The agent has to decide between two actions - moving the cart left or right - so that the pole attached to it stays upright. Conference: ICML 2020. This bootstrapped approach is what led DeepMind, a subsidiary of Alphabet ( GOOG , GOOGL ), to a dramatic victory over two professional StarCraft players: DeepMind’s agent, AlphaStar Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. ) Remember how you learned to ride a bike? More than likely  2017년 6월 3일 기본적으로 Reinforcement Learning의 목표는 expected return을 최대치로 하는 policy를 학습하는 것이며, 아래의 공식과 같은 Optimal action-value . 7 Nov 2020 qualitatively compare existing RL-based routing protocols. 3. com We propose to perform multi-task reinforcement learning using a single base policy network with multiple modules. Q(s t,a t) ← (1 − α)Q(s t,a t)+α[r t+1 +max a Q(s t+1,a)] Abstract We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. Abstract: Reinforcement learning (RL), which is  We present an end-to-end framework for solving the Vehicle Routing Problem. Let’s understand this with a simple example below. A recent paper [3] introduced the problem of determining In this paper we demonstrate that a reinforcement learning algorithm of the Q-learning family, based on a customized exploration and exploitation strategy, is able to learn optimal actions for the routing autonomous taxis in a real scenario at the scale of the city of Singapore with pick-up and drop-off events for a fleet of one thousand taxis. Ramy E. E- mail The com- plexity of training RL with large state-action space becomes an obstacle of deploying RL-based packet routing. 2. Subject(s):, Maze routing · Reinforcement Learning. Recent studies conrm the ability of DRL in solving complex routing problems; however, its performance in the network with QoS- sensitive ows has not been addressed. 11, pp 547-558, Sept 2019 reinforcement learning, good routing configurations. Quality of service. Reinforcement learning (RL) is one of the most important algorithms that has a significant contribution to the development of AI [13–15]. 1993/1994 to improve packet routing in communication  We study the feasibility of implementing an adaptive routing policy using the Q- Learning algorithm which learns sequences of actions from delayed rewards. In most previous works on reinforcement learning for network optimization, routing and spectrum access are tackled as separate tasks; further, the wireless links in the network are assumed to be fixed, and a different agent is trained for each In this paper, we leverage deep reinforcement learning (DRL) for router selection in the network with heavy traf˝c, aiming at reducing the network congestion and the length of the data transmission path. Agent Environment action s t a t reward r t r t+1 s t+1 state • Q-learning [Watkins 1989] is a well known method for learning a policy (mapping from states to actions). We present three RL-based methods for learning Then introduced a novel routing scheme referred to as two‐hop relay selection by multi‐metric based reinforcement learning algorithm. gather the information about the nodes like energy consumption, link quality. In this approach, we train. Boyan Book Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications survey on the existing various approaches towards routing policy over dynamically changing networks with the view of reinforcement learning. Computer   12 Feb 2018 We present an end-to-end framework for solving. Budget ₹1500-12500 INR. Jun 18, 2019 · DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks. In simple experiments involving Mar 15, 2018 · Deep-RMSA: A Deep-Reinforcement-Learning Routing, Modulation and Spectrum Assignment Agent for Elastic Optical Networks. Context. implement Q-learning technique 2. We present two applications of reinforcement learn-ing methods to the mobilized ad-hoc networking domain and demonstrate some promising empirical results under a Jun 20, 2019 · Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. Yoo1. However, existing proposals fail to achieve good results, often under-performing traditional routing techniques. Q. Authors: Gautham Nayak Seetanadi, Karl-Erik Årzén, Martina Maggio. The results also show that the reinforcement learning ag Deep Reinforcement Learning Enabled Network Routing Optimization Approach with an Enhanced DDPG Algorithm. Maguire, 2. Recently, an attention model is proposed to solve routing problems. 946. The goal of routing is to deliver packets with minimum delay. To cite this article: Zheng Wang et al 2020 J. INTRODUCTION. Machine Learning (ML) Reinforcement learning based multipath routing Python & Investigación Projects for ₹1500 - ₹12500. They treat the input as a graph and feed it to a modified Transformer Sequential transfer in reinforcement learning with a generative model Authors: Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli. Apr 06, 2019 · Learning to Solve Problems Without Human Knowledge. Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. And Deep Learning, on the other hand, is of course the best  8 Sep 2016 (Video courtesy of Mark Harris, who says he is “learning reinforcement” as a parent. This aims to maximise the durability of the entire network while preserving usability. It applies a traditionalRLapproachcalledQ-learning[ , ],whichisa In this paper we describe a self-adjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Page 2. ( VRP) using reinforcement learning. In this model, the state of an instance is represe Cross Layer Routing in Cognitive Radio Network. RL consists of an agent and an environment in which the agent explores the environment by taking actions and reaches an optimal policy for the system [16]. Optical Networks. This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v0 task from the OpenAI Gym. It applies Q learning to adaptive network routing techniques to improve overall performance of the Apr 28, 2020 · In this blog post, we will guide you through the basic concepts of Reinforcement Learning and how it can be used to solve a simple order-pick routing problem in a warehouse using Python. In the context of networking, there is a growing trend in the research community to apply DRL algorithms to optimization problems such as routing. Existing methods address this using static routing methods considering neither the demands of requests nor the transfer of customers between vehicles during route planning. tion of reinforcement learning. Current trend to decouple the network intelligence from the network devices enabled by Software-Defined Networking (SDN) provides a centralized implementation of network Reinforcement Learning. Jan 19, 2017 · Reinforcement Learning is learning what to do and how to map situations to actions. The end result is to maximize the numerical reward signal. RL should be able to utilize the underlying mechanics of the environment for its own advantage. Q-routing [ 8] is the first RL-based routing algorithm. CHOI, D. Master of Science in. The deep neural network is used to characterize the input instance for constructing a feasible solution incrementally. 1. The Q-Routing algorithm adapts a network's routing policy based on local infor Keywords: Three-Dimensional FPGA, Placement and. We consider network routing as a multi-agent, partially observable Markov decision process (POMDP). Our pre-liminary findings suggest that this is a promising direction for improving upon today’s intradomain TE. Modulation and Spectrum Assignment Agent for Elastic. aware reinforcement learning (RL) based routing algorithm. DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. In this paper, we present a comprehensive survey of RL-based routing protocols for MANETs. As visualized in Figure2, instead of finding discrete routing paths to connect the modules for different tasks, we perform soft modularization: we utilize another routing network Sep 02, 2020 · Reinforcement Learning was originally developed for Markov Decision Processes (MDPs). Uçan , and Muhammad Ilyas ALTINBAŞ University, Engineering and Naturel Science, Mahmutbey, Istanbul, Turkey Q-learning is one of the easiest Reinforcement Learning algorithms. We believe that our investigation below but scratched the sur-face of data-driven routing. Reinforcement Learning (RL) • Basic theme of RL is learning to behave optimally under uncertainty based on feedback from the environment. In particular, as we train over a greater number of chip blocks, our method becomes better at rapidly generating optimized placements for Machine learning used to be either supervised or unsupervised, but today it can be reinforcement learning as well! Here we’ll start with a very simple Python 1. 2 Multi-agent cooperation and coordination. Optimizing the runtime performance of a Network-on-Chip (NoC) necessitates a continuous learning framework. Genre: Thesis. A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of. Cognitive Radio (CR) was first coined by Mitola et al. SDN. Machine learning (ML) . Only local communication is used to keep accurate statis-tics at each node on which routing policies lead to minimal delivery times, In simple experiments involving a 36-node, irregularly connected network, Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks. Jose Suarez-Varela*, Albert Mestres*, Junlin yut, Li Kuangt, Haoyu Fengt,. the Traditional Reinforcement Learning Approach to Routing is section presents CRQ-routing that takes account of the PUs and SUs network performance by minimizing SUs interference to PUs along a route without signi cantly jeopardizing SUs network-wide performance. The problem with Q-earning however is, once the number of states in the environment are very high, it becomes difficult to implement them with Q table as the size would become very, very large. e. The Q  Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in  Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks. Lingyu Meng, Wen Yang, Bingli Guo, and Shanguo Huang. Reinforcement learning based multipath routing . Reinforcement learning methods can be used to control both packet routing decisions and node mo-bility, dramatically improving the connectivity of the net-work. Conference: International  Reinforcement learning (RL). The learner is not told which action to take, but instead must discover which action will yield the maximum reward. Introduction. Only local Oct 01, 2019 · In order to accomplish the validation of the AMAM framework with reinforcement learning, computational experiments were performed, using, as a case study for this purpose, the Vehicle Routing Problem with Time Window and Unrelated Parallel Machine Scheduling Problem with Sequence-Dependent Setup Times. A. . the Reinforcement learning technology to software defined network routing algorithm, and propose the routing algorithm based on Q-learning. Abstract: This paper demonstrates Deep-RMSA, a deep reinforcement learning based self-learning RMSA agent that can learn successful policies from dynamic network operations while realizing cognitive and autonomous RMSA in EONs. Our model represents a parameterized stochastic policy, and by applying a policy Apr 23, 2020 · RL training is guided by a fast-but-approximate reward signal calculated for each of the agent’s chip placements using the weighted average of approximate wirelength (i. Author Information  23 Oct 2019 Scalable Routing with Deep Reinforcement Learning. Difference learning with experience replay) to approximate the value function and a routing heuristic based on&nb We present an end-to-end framework for solving the Vehicle Routing Problem. [3] is a ML technique that attempts to learn about the optimal action with respect to the dynamic operating environment. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. 2016010104: Cognitive radio networks (CRNs) can provide a means for offering end-to-end Quality of Service (QoS) required by unlicensed users (secondary users. In this approach, we train a single policy. Feb 11, 2021 · itself. RL is popular for its trial-and-optimize scheme. In the literatu… Q-Routing is an adaptive routing algorithm that was proposed by Boyan and Littman in 1993/1994 to improve packet routing in communication networks. With the advance of AI and big data, this project aims to solve vehicle routing problems (VRP) using reinforcement learning. We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). B. Specifically, Q-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP). Reinforcement Learning for Solving the Vehicle Routing Problem - OptMLGroup/ VRP-RL. Machine Learning (ML) Reinforcement learning based multipath routing Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. As a starting point it will discuss the Belman-Ford shortest path algorithm, implemented to routing. Adaptive Routing with Guaranteed Delay Bounds using Safe-Reinforcement Learning. Mitola and G. In Figure 1 (a), if we regard the network as an environment and cognitive routing controller(s) as intelligent agent(s), the architecture of cognitive routing enabled network is similar with the reinforcement learning (RL) framework in Figure 1 (b). Xiaoliang Chen1, Jiannan Guo2, Zuqing Zhu2, Roberto Proietti1, Alberto Castro1, S. Currently, artificial intelligence methods like Reinforcement Learning (RL) are widely used to design adaptive routing strategies for MANETs. Learn problem formulation, Q learning and  Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize  Reinforcement Learning is a very general framework for learning sequential decision making tasks. Tauqeer Safdar Malik 1 and  In (Boyan & Littman, 1994), a distributed adaptive traffic control scheme based. Published in: 2018 Optical Fiber Communications Conference and Exposition (OFC) Deep Reinforcement Learning (DRL) is an emerging technique that is able to cope with such complex problem. Department Head: Sameer Sharma. M. Task. In this paper, we present a dynamic and demand aware fleet management framework that is informed by a representation learned from Deep Reinforcement Learning. The router has to develop a behaviour by means of trial and error interaction with its dynamic environment [5, 6]. It is a distributed reinforcement learning scheme for packet routing in computer networks.