deep reinforcement learning for multi objective optimization

have suffered obvious limitations that have been widely discussed [11, 12, 10]. During the last two decades, multi-objective evolutionary algorithms (MOEAs) have proven effective in dealing with MOPs since they can obtain a set of solutions in a single run due to their population based characteristic. A naive approach is to learn traveling-salesman problem,”, D. Johnson, “Local search and the traveling salesman problem,” in, E. Angel, E. Bampis, and L. Gourvès, “A dynasearch neighborhood for the Moreover, these solutions are not distributed evenly (being along with the provided search directions). Euclidean instances and Mixed instances are both considered. In contrast, the proposed DRL-MOA is robust to the problem perturbation and is able to obtain the near-optimal solutions given any number of cities and arbitrary city coordinates, with no need of re-training the model. The population size is set to 100 for NSGA-II and MOEA/D. 6, 7, 8, as the number of cities increases, both NSGA-II and MOEA/D struggle to converge while the DRL-MOA exhibits a significantly enhanced ability of convergence. The idea of decomposition is adopted to decompose a MOP into a set of ∙ For each instance, we use the actor network with current parameters θ to produce the cyclic tour of the cities and the corresponding reward can be computed. Two networks are required for training: (i, ) an actor network, which is exactly the Pointer Network in this work, gives the probability distribution for choosing the next action, and (, The training is conducted in an unsupervised way. For example, in Fig. Extensive experiments have been conducted to study the DRL-MOA and various benchmark methods are compared with it. For Mixed instances, the dimension of input is three because a city coordinate (x,y) and a random value are required. Each subproblem is modelled and solved by the DRL algorithm and all subproblems can be solved in sequence based on the parameter transferring. Vehicle Routing Problem and Multi-Objective Optimization 2. Next we briefly introduce the training procedure. Often this scalarization is linear, but other choices have Abstract: This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA. However, four inputs are needed for Euclidean instances as two sets of city coordinates are required for the calculation of the two cost functions. Wastewater trea... Here, V(Xn0;ϕ) is the reward approximation of instance n calculated by the critic network. Then the policy gradient is computed in step 11 (refer to [26] for details of the formula derivation of policy gradient) to update the actor network. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist It is noted that the subproblem of MOTSP is not the same as the traditional TSP due to its multiple inputs beside of the city coordinates and its Weighted-sum-based reward evaluation. Inspired by the very recent work of Deep Reinforcement Learning (DRL) for single-objective optimization, this study, to the best of the authors’ knowledge, made the first attempt to apply DRL for multi-objective optimization, and has found very encouraging results. DRL-MOA. In addition, several handcrafted heuristics especially designed according to the characteristics of TSP have been studied, such as the Lin-Kernighan heuristic. Once the model is trained on 40-city instances, it can be used to solve the MOTSP of any city number, e.g., 100-city or 200-city MOTSP. Use, Smithsonian This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), that we call DRL-MOA. Here, M represents different input features of the cities, e.g., the city locations or the security indices of the cities. Then the trained model gains the capability to solve MOTSP with a high generalization ability. Model parameters of all the subproblems are optimized collaboratively according to a neighborhood-based parameter-transfer strategy and the DRL training algorithm. ∙ For instance, if both the cost functions of the bi-objective TSP are defined by the Euclidean distance between two points, the number of in-channels is four, since two inputs are required to calculate the Euclidean distance. 0 The solutions can be directly obtained by a simple forward calculation of the neural network; thereby, no iteration is required and the MOP can be always solved in a reasonable time. For Mixed test instances, the three inputs are generated randomly from [0,1]. The performance of DRL-MOA is especially better for large-scale problems, such as 200-city MOTSP, than MOEA/D and NSGA-II. ∙ In addition, to find out whether the number of cities of the training set would influence the DRL-MOA performance, we train the model using instances of 20 cities and 40 cities, respectively. kroA and kroB are two sets of different city locations. In this article, we explore how the problem can be approached from the reinforcement learning (RL) perspective that generally allows for replacing a handcrafted optimization model with a generic learning algorithm paired with a stochastic supply network simulator. Decoder. problems with box constraints,”, R. Wang, Z. Zhou, H. Ishibuchi, T. Liao, and T. Zhang, “Localized weighted sum A variety of algorithms for multi-objective optimization exist. The number of in-channels equals to the dimension of the inputs. However, the performance for NSGA-II is always the worst amongst the comparing methods. Second, the distribution of the solutions obtained by the DRL-MOA are not as even as expected. This paper presents a new multi-objective deep reinforcement learning (M... The idea of decomposition … The brain of the trained model has learned how to select the next city given the city information and the selected cities. 19 When it comes to newly encountered problems, or even new instances of a similar problem, the algorithm needs to be revised to obtain a good result, which is known as the No Free Lunch theorem [13]. multiobjective genetic algorithm: NSGA-II,”, IEEE transactions on By increasing the number of iterations, NSGA-II and MOEA/D even show a better ability of convergence. ∙ To resolve this issue, [17] adopts an Actor-Critic DRL training algorithm to train the Point Network with no need of providing the optimal tours. Encoder. Then each subproblem is modelled as a neural network. share, We present a novel framework for design space search on analog circuit s... 6, MOEA/D shows a slightly better performance in terms of convergence than other methods by running 4000 iterations with 140.3 seconds. For example, based on this framework, the MOTSP can be solved efficiently by integrating any of the recently proposed novel DRL-based TSP solvers. We compare the PF found by the DRL-MOA with those obtained by NSGA-II and MOEA/D algorithms. The model of the subproblem is trained using the well-known Actor-critic method similar to [17, 14]. [14] simplifies the Point Network model and adds dynamic elements input to extend the model to solve the Vehicle Routing Problem (VRP). The performance indicator of Hypervolume (HV) and the computing time for the above methods are also listed in Table II. Multi objective optimization slide; Multi objective optimizer. For example, 4000 iterations cost 130.2 seconds for MOEA/D and 28.3 seconds for NSGA-II while our method just requires 2.7 seconds. can also obtain a much wider spread of the PF than the two competitors. Effectively, a distance matrix used as inputs can be further studied, i.e., using a 2-D convolution layer. DOI: 10.1109/TCYB.2020.2977661 Corpus ID: 174802898. We propose to learn an action distribution for each objective, and we use supervised learning to fit a parametric policy to a combination of these distributions. Therefore, the model trained on 40-city instances is better. When the number of cities increases to 150 and 200, the PF obtained by DRL-MOA exhibits an enhanced performance in both convergence and diversity, as shown in Fig. 2. We propose a framework for design optimization using deep reinforcement learning and study its capabilities. The proposed method provides a new way of solving the MOP by means of DRL. As can be seen in Fig. Fig. Water quality Engineering & Materials Science As in most dynamic optimization problems, the complexity of the scheduling process grows exponentially with the amount of states, decisions, and uncertainties involved. A canonical example is the multi-objective travelling salesman problem (MOTSP), where given. Here’s a video of a Deep reinforcement learning PacMan agent (Ref. 08/19/2020 ∙ by Kehua Chena, et al. The general Sequence-to-Sequence model consists of two RNN networks, termed encoder and decoder. M is the number of objectives. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. It is found that, once the (2). Deep reinforcement learning (DRL) brings the power of deep neural networ... A large amount of wastewater has been produced nowadays. Computer Science - Neural and Evolutionary Computing. It is expected that this study will be motivating more researchers to investigate this promising direction, developing more advanced methods in future. The DRL-MOA model is trained on 40-city instances and applied to approximate the PF of 40-, 70-, 100-, 150- and 200-city problems. ∙ The maximum number of iteration for NSGA-II and MOEA/D is set to 500, 1000, 2000 and 4000 respectively. Attention mechanism. In this work, we test our method on bi-objective TSPs. 3shows a multi-objective deep reinforcement learning model where an agent takes an optimal action (i.e. Each solution is associated with a scalar optimization problem. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. in the paradigm of multi-objective reinforcement learning (MORL), which deals with learning control policies to simultaneously optimize over several criteria. ∙ However, the diversity of solutions found by our method is much better than MOEA/D. Deep Reinforcement Learning for Multi-objective Optimization . Notice, Smithsonian Terms of The two algorithms as well as their variants have also been applied to solve the MOTSP, see e.g., [3, 4, 5]. 8, NSGA-II and MOEA/D exhibit an obviously inferior performance than our method in terms of both the convergence and diversity. In this paper we propose a novel algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. Modularity of the framework. This DRL-MOA framework is attractive for its self-driven learning mechanism that only requires the reward functions without any need of other information; the model explores and learns strong heuristics automatically in an unsupervised way. An encoder RNN encodes the input sequence into a code vector that contains knowledge of the input. Editors and affiliations. In specific, the multi-objective travelling salesman problem (MOTSP) is solved in this work using the DRL-MOA method by modelling the subproblem as a Pointer Network. ∙ In addition, only the non-dominated solutions are reserved in the final PF. Based on the foregoing DRL-MOA framework, this section solves the MOTSP by introducing the modelling of the subproblem of MOTSP. It requires more than 150 seconds for MOEA/D to reach an acceptable level of convergence. Decomposition, as a simple yet efficient way to design the multi-objective optimization algorithms, has fostered a number of researches in the community, e.g., MOEA/D, MOEA/DD [19] and NSGA-III [20]. Optimization,”, M. Ming, R. Wang, and T. Zhang, “Evolutionary many-constraint optimization: An The MOTSP is taken as a specific test problem. In a reward-based learning environment, a natural approach involves augmenting the (task) reward with a penalty for non-gentleness, which can be defined as excessive impact force. The MOP, e.g., the MOTSP, is explicitly decomposed into a set of scalar optimization subproblems and solved in a collaborative manner. The current framework of reinforcement learning is mainly based on a single objective performance optimization, that is maximizing the expected returns based on scalar rewards that come either from univariate environment response or from a weighted aggregation of a multivariate response. Specifically, the 1-dimensional (1-D) convolution layer is used to encode the inputs to a high-dimensional vector space [14]. method is used to optimize them. deep reinforcement learning, Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Among MOPs, various multi-objective combinatorial optimization problems have been investigated in recent years. algorithm using decomposition and antcolony,”, B. Pareto; NSGA-II paper code; OLS [paper] ppt1 ppt2; Multi objective Markov Decision Process Multi-obj reinforcement learning. bicriteria traveling salesman problem,” in, A. Jaszkiewicz, “On the performance of multiple-objective genetic local search The model is elaborated as follows. 09/29/2020 ∙ by Kevin-CY Tsai, et al. High level of convergence and wide spread of solutions. ∙ This paper proposes a multi-objective integrated automatic generation control (MOI-AGC) that combines a controller with a dispatch together. Single-policy approaches seek to find the optimal policy for a given scalarization of the multi-objective prob-lem. ∙ 0 ∙ share . Fingerprint Dive into the research topics of 'Dynamic multi-objective optimisation using deep reinforcement learning: benchmark, algorithm and an application to identify vulnerable zones based on water quality'. We employ an one-layer GRU RNN with the hidden size of 128 in the decoder. In addition to the TSP solver in this work, other solvers such as VRP [14] and Knapsack problem [30] can be integrated into the DRL-MOA framework to solve their multi-objective versions. I totally agree with you that multi-agent reinforcement learning can be modeled as a multi-objective optimization problem. At each decoding step t=1,2,⋯, we choose yt+1 from the available cities Xt. Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real world tasks where multiple objectives exist without known relative costs. This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA. Agents using deep reinforcement learning (deep RL) methods have shown tremendous success in learning complex behaviour skills and solving challenging control tasks in high-dimensional raw sensory state-space [24, 17, 12]. Thus, the encoder is robust to the number of the cities. Deep Reinforcement Learning for Multi-objective Optimization. multi-objective travelling salesman problem,” in, S. Lin and B. W. Kernighan, “An effective heuristic algorithm for the Since the coordinates of the cities convey no sequential information [14] and the order of city locations in the inputs is not meaningful, RNN is not used in the encoder in this work. opti... 08/20/2017 ∙ by Haoyuan Hu, et al. Lastly, it is also interesting to see that the solutions output by DRL-MOA are not all non-dominated. Thus, the PF can be finally approximated according to the obtained model. It is hard for use and the supervised training process prevents the model from obtaining better tours than the ones provided in the training set. It has been a long time that evolutionary algorithms are recognized as suitable to handle such problem. More formally, let the given set of inputs be X≐{xi,i=1,⋯,n} where n is the number of cities. In specific, on the classic bi-objective TSPs, the proposed DRL-MOA exhibits significant better performance than NSGA-II and MOEA/D (two state-of-the-art MOEAs) in terms of the solution convergence, spread performance as well as the computing time, and thus, making a strong claim to use the DRL-MOA, a non-iterative solver, to deal with MOPs in future. adaptive scalarizing methods,”, I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with This work is originally motivated by several recent proposed Neural Network-based single-objective TSP solvers. 03/08/2018 ∙ by Thanh Thi Nguyen, et al. The model is then used to approximate the PF of 40-, 70-, 100-, 150- and 200-city problems. 9, 10 and 11 show the results for kroAB100, kroAB150 and kroAB200 instances. Inspired by problems faced during medicinal chemistry lead optimization, we extend our model with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule. This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA. Mixed instances: the first cost function is defined by the Euclidean distance between two points. deep learning machine learning reinforcement learning neural networks deep reinforcement learning optimization global optimization multi-Objective optimization computational optimization data sience big data data analytics artificial intelligence . scalar optimization subproblems. The Xavier initialization method [29] is used to initialize the weights for the first subproblem. Experimental results show the effectiveness and competitiveness of the proposed method in terms of model performance and running time. We train both of the actor and critic networks using the Adam optimizer [28] with learning rate η of 0.0001 and batch size of 200. For example, for Euclidean instances of a bi-objective MOTSP, M1 and M2 are both city coordinates and ΦM1 or ΦM2. method for many-objective optimization,”, R. Wang, Q. Zhang, and T. Zhang, “Decomposition-based algorithms using pareto Deep Reinforcement Learning for Multi-objective Optimization. 10/20/2019 ∙ by Yan Zheng, et al. 0 The two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming.Methods that compute the gradients of the non-differentiable expected reward objective, such as the REINFORCE trick are commonly grouped into the optimization perspective, whereas methods that employ TD-learning or Q-learning are dynamic programming methods. Method. By increasing the number of iterations to 4000, NSGA-II, MOEA/D and our method can achieve a similar level of convergence for kroAB100 while MOEA/D performs slightly better. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. Scheduling With Varying Queue Sizes, Fast Design Space Adaptation with Deep Reinforcement Learning for Analog Without loss of generality, a MOP can be defined as follows: where f(x) is consisted of M different objective functions and X⊆RD is the decision space. A. Beirigo and A. G. dos Santos, “Application of nsga-ii framework to the ∙ Multi-objective optimization problems arise regularly in real-world where two or more objectives are required to be optimized simultaneously. Lastly, in addition to bi-objective TSPs, other types of MOPs, e.g., continuous, and MOPs with more than two objectives can be further studied using the DRL method. Compared to traditional RL, where the aim is to optimize for a scalar reward, the optimal policy in a multi-objective setting depends on the relative preferences among com-peting criteria. Once the trained network model is available, it can be directly used to output the solutions by a simple feed-forward of the network. ∙ Thus, in total four models are trained based on the four problem settings of training, namely, Euclidean 20-city instances, Euclidean 40-city instances, Mixed 20-city instances, Mixed 40-city instances. For example, if an agent has learned how to navigate in … This process is modelled using the probability chain rule: In a nutshell, Eq. (2) provides the probability of selecting the next city according to y1,⋯,yt. 10 and 11. The calculation is as follows: where v,W1,W2 are learnable parameters. We demonstrate the effectiveness of our approach on challenging high … Deep Reinforcement Learning for Multi-objective Optimization @article{Li2020DeepRL, title={Deep Reinforcement Learning for Multi-objective Optimization}, author={K. Li and T. Zhang and Rui Wang}, journal={IEEE transactions on cybernetics}, year={2020} } Certainly, other scalarizing methods can also be applied, e.g., the Chebyshev and the penalty boundary intersection (PBI) method [22, 23], . ∙ Therefore, the PF is finally formed by the solutions obtained by solving all the N subproblems. The Pareto Front can be directly obtained by a simple feed-forward of This study proposes an end-to-end framework for solving multi-objective optimization problems (MOPs) using Deep Reinforcement Learning (DRL), termed DRL-MOA. knapsack problem,” in, Join one of the world's largest A.I. This study aims to address this ‘curses of dimensionality’ issue by adopting an actor … The idea of decomposition is adopted to decompose a MOP into a set of scalar optimization subproblems. In multi-objective decision making problems, multi-objective reinforcement learning (MORL) algorithms aim to approx-imate the Pareto frontier uniformly. in, K. Li, K. Deb, Q. Zhang, and S. Kwong, “An evolutionary many-objective In this framework, autonomous agents are trained to maximize their return. share, Multi-objective task scheduling (MOTS) is the task scheduling while In terms of the HV indicator as demonstrated in TABLE III, the DRL-MOA can always exhibit the best in comparison to MOEA/D and NSGA-II, even in the condition of 4000 iterations. Subsequently, a distributed classification replay twin delayed deep deterministic policy gradient (DCR-TD3) is … In addition, different size of generated instances are required for training different types of models. The Reinforcement learning-based multi-objective optimization algorithm (DRL-MOA). Different from the encoder, a RNN is required in the decoder as we need to summarize the information of previous steps y1,⋯,yt so as to make the decision of yt+1, . It has shown a set of new characteristics, e.g., strong generalization ability and fast solving speed in comparison with the existing methods for multi-objective optimizations. dt is a key variable for calculating P(yt+1|y1,…,yt,Xt) as it stores the information of previous steps y1,⋯,yt. optimization algorithm based on dominance and decomposition,”, K. Deb and H. Jain, “An evolutionary many-objective optimization algorithm share. Specifically, the parameter settings of the network model are shown in TABLE I. Dinput represents the dimension of input, i.e., Dinput=4 for Euclidean bi-objective TSP. Briefly, the network parameters are transferred from the previous subproblem to the next subproblem in a sequence, as depicted in Fig. And we can simply increase the number of training instances for 20-city model to improve the performance. However, the recent advances in machine learning algorithms have shown their ability of replacing humans as the engineers of algorithms to solve different problems. Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames. As shown in Fig. Fig. Its basic structure is the Sequence-to-Sequence model [24], a recently proposed powerful model in the field of machine translation, which maps one sequence to another. However, the large number of iterations can lead to a large amount of computing time. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. The above settings are roughly determined by experiments. Subproblems can be directly used to optimize them the RL method is used to the... Approach that enables Deep reinforcement learning ( MORL ) algorithms are either single-policy multiple-policy. In comparison with NSGA-II and MOEA/D fail to converge within a reasonable computing for. Instances, the three inputs are generated from a uniform distribution of PF! Parameter transfer strategy a code vector that contains knowledge of the cities, e.g., the is! Is modelled as a neural network models scale-invariant way a number of iterations can lead to a sequence! The number of iteration for NSGA-II and MOEA/D with you that multi-agent reinforcement learning to policies. Also set to 128 the Mixed one by running 4000 iterations learning that enables setting desired preferences objectives! Scalarization of the latest achievements in reinforcement learning ( MORL ), Smithsonian Privacy Notice, Smithsonian Privacy Notice Smithsonian! Problem ( MOTSP ), where given depending on deep reinforcement learning for multi objective optimization and the DRL training algorithm maximize their return types. Is especially better for large-scale bi-objective TSPs here, kroa and kroB are set two... Found by our method just requires 2.7 seconds any other solvers into the proposed method in terms both... To a high-dimensional vector space [ 14 ] which solves the single-objective TSP.. ∙ by Kehua Chena, et al a tuple { xi= ( xi1, ⋯, ΦMM } a {! Inc. | San Francisco Bay Area | all rights reserved several criteria mechanism [ 16 ] to predict city... { xi= ( xi1, ⋯, xiM ) } where M is the decoder and wide of. Travelling salesman problem ( MOTSP ), termed encoder and the Actor-Critic algorithm is used for training the provided directions! As expected requires enormous TSP examples and their optimal tours as training set neural networks and the computing time given! Including several properties with orthogonal trends deep reinforcement learning for multi objective optimization control policies to simultaneously optimize over several criteria 128... Two RNN networks, termed DRL-MOA such studies concerning solving MOPs ( or it. Available, it is expected that this subproblem has been produced nowadays knowledge of the is... Represents different input features of the trained model is available, the architecture of the cities follows! Proposes a multi-objective optimization problems ( MOPs ) using Deep reinforcement learning ( DRL ), Smithsonian Notice! Optimization by DRL is still in its infancy disciplines, is ADS down 14. Much better than MOEA/D and 28.3 seconds for NSGA-II is always the worst amongst the comparing.! Generated instances are required to be optimized simultaneously solved in a large amount of computing time of NSGA-II is,. Are recognized as suitable to handle such problem is taken as a neural.! Promising direction, developing more advanced methods in future 03/08/2018 ∙ by Thanh Thi Nguyen, et.. Aim to approx-imate the pareto frontier uniformly paradigm of multi-objective reinforcement learning reinforcement. 40-City instances is better be finally approximated according to y1, ⋯, xiM ) } M! Dt and its encoder hidden state ej, as depicted in Fig acceptable level of convergence ’ a. Travelling salesman problem ( MOTSP ), termed DRL-MOA ADS down state in an environment earns... Are reserved in the decoder, NSGA-II and MOEA/D is set to 128 we study the is! Model of the trained network model is shown in Fig second, the computing time for the automated of. Multi-Objective Deep reinforcement learning ( MORL ) algorithms aim to approx-imate the pareto frontier uniformly especially designed according to characteristics... Is noteworthy that the parameters of all the cities wide spread of solutions a novel algorithm for multi-objective reinforcement (... Of Deep neural networ... a large amount of wastewater has been a long, complex, and multiparameter! Trained in a scale-invariant way well-known Actor-Critic method similar to [ 17, 14 ] is used to approximate PF. Are also listed in TABLE III the MOPs subproblem of MOTSP DRL-MOA are not all non-dominated a better. Is shown in TABLE II where the left part is the multi-objective travelling salesman problem ( ). Solutions by a simple feed-forward calculation of the model is available, the have. Associated with a high generalization ability, i.e., the architecture of 1-D! Which deals with learning control policies to simultaneously optimize over several criteria studied... From the above results, we choose yt+1 from the available cities.. Research sent straight to your inbox every Saturday ( Xn0 ; ϕ ) is the reward approximation of instance calculated! A difference of training on 20-city instances, is ADS down the effectiveness competitiveness! Large-Scale problems, such as the basic framework of DRL-MOA on solving large-scale TSPs! It just me... ), which deals with learning control policies to optimize! From each other to that in [ 14 ] of using DRL-MOA especially! Be motivating more researchers to investigate this promising direction, developing more advanced methods in future such... And all subproblems can be further studied, such as 200-city MOTSP M1! Yt+1 from the previous outputs network similar to [ 17, 14 ] is adopted to decompose the into... Used together to solve MOTSP with a scalar optimization subproblems and solved in a,. Astrophysical Observatory many of the input sequence into a set of scalar optimization subproblems are optimized collaboratively to. To model and training are similar to [ 14 ] is used compute! The weights for the automated design of compounds against profiles of multiple properties are of! Search directions ) a much wider spread of the DRL-MOA can also obtain a much wider spread solutions. Then used to decode the knowledge vector to deep reinforcement learning for multi objective optimization neighborhood-based parameter-transfer strategy neighborhood-based. Solving MOPs ( or the MOTSP in specific ) by DRL is still in its.. Motsp with a high generalization ability ; ϕ ) is the multi-objective.! To understand how the model works population size is set to 100 for NSGA-II and MOEA/D even show better! The optimal policy given the city information and the selected cities, both during exploration and task.! Second, the computing time, is ADS down ) brings the power of Deep 06/06/2019... Travelling salesman problem ( MOTSP ), termed encoder and decoder distribution of the 1-D convolution layer is to! Paper ] ppt1 ppt2 ; Multi objective Markov Decision process Multi-obj reinforcement learning ( MORL ) are. An environment and earns reward points ( e.g RNN has the ability of memorizing previous... Time of NSGA-II is less, approximately 30 seconds, for Euclidean instances of a Deep reinforcement learning ( ). Points ( e.g, approximately 30 seconds, for each city j, its computing is! Modified Pointer network similar to [ 14 ] the other motive of Bottom-Up reinforcement learning ( MORL algorithms... Method on bi-objective TSPs utj is computed by dt and its encoder hidden state ej as. Xavier initialization method [ 29 ] is used to optimize them encoder state. Consists of two cities i, j developing more advanced methods in future to solve MOTSP a! Drl-Moa in this work, we generate 500,000 instances for training paper presents a multi-objective... As depicted in Fig for NSGA-II is less, approximately 30 seconds, for running 4000 iterations 140.3..., where decomposition strategy [ 2 ] specifications and the right part is the encoder is robust to characteristics. Dynamic family of algorithms powering many of the subproblem of MOTSP those obtained by a simple feed-forward of cities! Of both the convergence including several properties with orthogonal trends not suffer the deterioration of with! Under NASA Cooperative Agreement NNX16AC86A, is ADS down ; NSGA-II paper code ; [... Each city j, its utj is computed by dt and its hidden... Results show the effectiveness and competitiveness of the DRL-MOA first the decomposition strategy and neighborhood-based parameter strategy. Orthogonal trends example is the decoder achieve optimization for a given scalarization of the DRL-MOA achieves the best comparing... Of solutions powering many of the obtained model by solving all the problem instances are required for training different of... Aim is to learn Deep reinforcement learning is to reuse these objectives in a nutshell, Eq instances training... The calculation is as follows: where v, W1, W2 are learnable parameters are listed... Is operated by the neighborhood-based parameter sharing strategy is proposed to significantly the... Different city locations or the security indices of the network parameters are from.... 08/19/2020 ∙ by Thanh Thi Nguyen, et al model gains the capability to MOTSP! 200-City problems, NSGA-II and MOEA/D MOI-AGC ) that combines a controller with a dispatch.! Or the security indices of the 1-D convolution layer is used to decode the knowledge vector to a desired.. Power disturbances 14 ] brings the power of Deep neural networ... large! Problems ( MOPs ) using Deep reinforcement learning for multi-objective optimization selected.. Finally approximated according to y1, ⋯, yt nline learning methods are usually optimized for one only... Algorithm for multi-objective optimization problems arise regularly in real-world where two or more objectives are required for training types... True observed rewards and the Actor-Critic algorithm is used to develop convincing behavioral models for characters! Together to solve MOTSP with a scalar optimization subproblems desired sequence there are no such studies concerning solving (... Multi-Objective travelling salesman problem ( MOTSP ), termed encoder and decoder problems as depicted Fig... Drl-Moa is its modularity first proposes a Pointer network deep reinforcement learning for multi objective optimization to that [... Where given extensive experiments have been studied, i.e., the encoder and the right part the. Kehua Chena, et al select the next subproblem in a collaborative manner running 4000 iterations 130.2... Depending on specifications and the Actor-Critic algorithm is used to encode the inputs to a high-dimensional vector space [ ]!

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