Deep reinforcement learning with credit assignment for combinatorial optimization
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. •. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. •. Model-free and model-based reinforcement learning algorithms can be connected to solve large-scale problems.
Deep Reinforcement Learning with Credit Assignment for Combinatorial
Abstract. Recent advances in Deep Reinforcement Learning (DRL) demonstrates the potential for solving Combinatorial Optimization (CO) problems. DRL shows advantages over traditional methods both ...
Chongxuan Li's Homepage
Deep reinforcement learning with credit assignment for combinatorial optimization Dong Yan ... Bilevel Programming in Hyperparameter Optimization Beijing Academy of Artificial Intelligence, BAAI-Live, online, 2021 Deep Generative Models: Representation, Learning and Inference ...
Deep reinforcement learning with credit assignment for combinatorial
Credit assignment. Recent advances in Deep Reinforcement Learning (DRL) demonstrates the potential for solving Combinatorial Optimization (CO) problems. DRL shows advantages over traditional methods both on scalability and computation efficiency. However, the DRL problems transformed from CO problems usually have a huge state space, and the ...
Deep Reinforcement Learning with Credit Assignment for Combinatorial
Reinforcement learning (RL) theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one ...
Deep reinforcement learning with credit assignment for combinatorial
DOI: 10.1016/j.patcog.2021.108466 Corpus ID: 244711689; Deep reinforcement learning with credit assignment for combinatorial optimization @article{Yan2021DeepRL, title={Deep reinforcement learning with credit assignment for combinatorial optimization}, author={Dong Yan and Jiayi Weng and Shiyu Huang and Chongxuan Li and Yichi Zhou and Hang Su and Jun Zhu}, journal={Pattern Recognit.}, year ...
Towards Practical Credit Assignment for Deep Reinforcement Learning
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards. Explicit credit assignment methods have the potential to boost the performance of RL algorithms on many tasks, but thus far remain impractical for general use. Recently, a family of methods called Hindsight Credit Assignment (HCA) was proposed, which ...
Advances in Deep Reinforcement Learning: Intrinsic Rewards, Temporal
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. Model-free and model-based ...
Deep Reinforcement Learning for Combinatorial Optimization: Covering
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the Multi-head Attention to capture the ...
Jiayi Weng
OpenAI - Cited by 2,127 - Reinforcement Learning - Machine Learning System ... Deep reinforcement learning with credit assignment for combinatorial optimization. D Yan, J Weng, S Huang, C Li, Y Zhou, H Su, J Zhu. Pattern Recognition 124, 108466, 2022. 19: 2022:
Temporal credit assignment in reinforcement learning
ABSTRACT. This dissertation describes computational experiments comparing the performance of a range of reinforcement-learning algorithms. The experiments are designed to focus on aspects of the credit-assignment problem having to do with determining when the behavior that deserves credit occurred. The issues of knowledge representation ...
PDF Deep Reinforcement Learning for Combinatorial Optimization: Covering
advances in deep learning have shown promising ability of solving NP-hard decision problems. A well-known example is the inspiring success of employing deep reinforcement learning to solve the game Go [5], which is a complex discrete decision problem. In the context of the advances attained by the deep learning in solving various decision tasks ...
Deep reinforcement learning with credit assignment for combinatorial
Highlights. •. Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. •. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. •. Model-free and model-based reinforcement learning algorithms can be connected to solve large-scale problems. •.
Awesome Machine Learning for Combinatorial Optimization Resources
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch Arxiv, 2022. paper. Zhang, Tianyu and Banitalebi-Dehkordi, Amin and Zhang, Yong. Learning to Branch with Tree-aware Branching Transformers Knowledge-Based Systems, 2022. journal, code. Lin, Jiacheng and Zhu, Jialin and Wang, Huangang and Zhang, Tao
[PDF] Deep Reinforcement Learning for Combinatorial Optimization
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP), where, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a ...
Deep Reinforcement Learning for Exact Combinatorial Optimization
Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving ...
Deep reinforcement learning for transportation network combinatorial
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. Model-free and model-based ...
Deep reinforcement learning with credit assignment for combinatorial
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. • Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. • Model-free and model-based reinforcement learning algorithms can be connected to solve large-scale problems. •
Deep Reinforcement Learning for Combinatorial Optimization: Covering
Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large-scale and require quick decisions.
Deep Reinforcement Learning for Combinatorial Optimization
Deep reinforcement learning proves its success in solving complicated combinatorial problems. This work studies essential issues of DRL and its applications in the disassembly planning problem for 4D-printed products and energy-aware task planning problems. From the aspect of the methodology, this work utilizes experimental results and theoretical analysis to prove the existence of compatible ...
Deep Reinforcement Learning for Combinatorial Optimization: Covering
This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the multihead attention (MHA) to ...
Anti-missile Firepower Allocation Based on Multi-agent Reinforcement
The WTA problem, a critical decision-making element in air defense and anti-missile systems, is a classic non-linear combinatorial optimization problem ... Abbeel, P., et al.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. ... A., Bulkan, S.: Weapon target assignment with combinatorial ...
Evolutionary Reinforcement Learning via Cooperative Coevolution
problem's population, also known as the credit assignment problem, ... for noisy optimization. Annals of Mathematics and Artificial Intelli-gence, 76:143-172, 2016. ... policy maximum entropy deep reinforcement learning with a stochastic actor. In International Conference on Machine Learning, pages 1861-
Tuning Reinforcement Learning Parameters for Cluster Selection to
The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable ...
Effective credit assignment deep policy gradient multi-agent
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. Model-free and model-based ...
PDF Department of The Air Force 24.b Small Business Technology Transfer
Candidate transport assignments are generated from a combinatorial explosion of factors and quantities, ... "Estimating risk and uncertainty in deep reinforcement learning." arXiv preprint arXiv:1905.09638 (2019); ... Reinforcement learning; nonlinear controls; optimization; satellite control; autonomy TPOC-1: John Brewer
IMAGES
VIDEO
COMMENTS
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. •. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. •. Model-free and model-based reinforcement learning algorithms can be connected to solve large-scale problems.
Abstract. Recent advances in Deep Reinforcement Learning (DRL) demonstrates the potential for solving Combinatorial Optimization (CO) problems. DRL shows advantages over traditional methods both ...
Deep reinforcement learning with credit assignment for combinatorial optimization Dong Yan ... Bilevel Programming in Hyperparameter Optimization Beijing Academy of Artificial Intelligence, BAAI-Live, online, 2021 Deep Generative Models: Representation, Learning and Inference ...
Credit assignment. Recent advances in Deep Reinforcement Learning (DRL) demonstrates the potential for solving Combinatorial Optimization (CO) problems. DRL shows advantages over traditional methods both on scalability and computation efficiency. However, the DRL problems transformed from CO problems usually have a huge state space, and the ...
Reinforcement learning (RL) theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one ...
DOI: 10.1016/j.patcog.2021.108466 Corpus ID: 244711689; Deep reinforcement learning with credit assignment for combinatorial optimization @article{Yan2021DeepRL, title={Deep reinforcement learning with credit assignment for combinatorial optimization}, author={Dong Yan and Jiayi Weng and Shiyu Huang and Chongxuan Li and Yichi Zhou and Hang Su and Jun Zhu}, journal={Pattern Recognit.}, year ...
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards. Explicit credit assignment methods have the potential to boost the performance of RL algorithms on many tasks, but thus far remain impractical for general use. Recently, a family of methods called Hindsight Credit Assignment (HCA) was proposed, which ...
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. Model-free and model-based ...
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the Multi-head Attention to capture the ...
OpenAI - Cited by 2,127 - Reinforcement Learning - Machine Learning System ... Deep reinforcement learning with credit assignment for combinatorial optimization. D Yan, J Weng, S Huang, C Li, Y Zhou, H Su, J Zhu. Pattern Recognition 124, 108466, 2022. 19: 2022:
ABSTRACT. This dissertation describes computational experiments comparing the performance of a range of reinforcement-learning algorithms. The experiments are designed to focus on aspects of the credit-assignment problem having to do with determining when the behavior that deserves credit occurred. The issues of knowledge representation ...
advances in deep learning have shown promising ability of solving NP-hard decision problems. A well-known example is the inspiring success of employing deep reinforcement learning to solve the game Go [5], which is a complex discrete decision problem. In the context of the advances attained by the deep learning in solving various decision tasks ...
Highlights. •. Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. •. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. •. Model-free and model-based reinforcement learning algorithms can be connected to solve large-scale problems. •.
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch Arxiv, 2022. paper. Zhang, Tianyu and Banitalebi-Dehkordi, Amin and Zhang, Yong. Learning to Branch with Tree-aware Branching Transformers Knowledge-Based Systems, 2022. journal, code. Lin, Jiacheng and Zhu, Jialin and Wang, Huangang and Zhang, Tao
This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP), where, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a ...
Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving ...
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. Model-free and model-based ...
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. • Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. • Model-free and model-based reinforcement learning algorithms can be connected to solve large-scale problems. •
Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large-scale and require quick decisions.
Deep reinforcement learning proves its success in solving complicated combinatorial problems. This work studies essential issues of DRL and its applications in the disassembly planning problem for 4D-printed products and energy-aware task planning problems. From the aspect of the methodology, this work utilizes experimental results and theoretical analysis to prove the existence of compatible ...
This article introduces a new deep learning approach to approximately solve the covering salesman problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the multihead attention (MHA) to ...
The WTA problem, a critical decision-making element in air defense and anti-missile systems, is a classic non-linear combinatorial optimization problem ... Abbeel, P., et al.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. ... A., Bulkan, S.: Weapon target assignment with combinatorial ...
problem's population, also known as the credit assignment problem, ... for noisy optimization. Annals of Mathematics and Artificial Intelli-gence, 76:143-172, 2016. ... policy maximum entropy deep reinforcement learning with a stochastic actor. In International Conference on Machine Learning, pages 1861-
The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable ...
Deep Reinforcement Learning is efficient in solving some combinatorial optimization problems. Credit assignment can be used to reduce the high sample complexity of Deep Reinforcement Learning algorithms. Model-free and model-based ...
Candidate transport assignments are generated from a combinatorial explosion of factors and quantities, ... "Estimating risk and uncertainty in deep reinforcement learning." arXiv preprint arXiv:1905.09638 (2019); ... Reinforcement learning; nonlinear controls; optimization; satellite control; autonomy TPOC-1: John Brewer