This paper presents an approach based on deep reinforcement learning to solve the combinatorial optimization problem of heavy-haul railway scheduling. The problem is modeled as a Markov decision process. Considering that the problem is characterized by complex process and strong constraints, a heavy-hau...
(2022) developed a deep reinforcement learning approach, a multi-agent approach, and a branch-price-and-cut algorithm, respectively, for the above three classes of problems. In addition, a few works have examined humanitarian relief network assessment issues, inventory-routing issues, aerial ...
Motivated by this, we attempt to use deep learning and reinforcement learning to solve the Steiner tree problem. In our approach, we first generate a candidate set for Steiner points based on the Steiner points' property, and then compute the node representation through the attention mechanism. ...
2.2.5 Reinforcement Learning: 这里用的就是 DQN 算法,具体可以参考其他博客。 2.3 Cross-lingual policy transfer: 这里进行跨语言策略迁移的目的是:为了处理数据量比较少的语言中的 active learning 问题。作者采用 Transfer learning 的方法,在数据量丰富的数据集上学习一个比较好的 policy,然后将这种策略应用到 数...
This paper introduces a novel approach to optimize routing in the urban settings by Deep Reinforcement Learning (DRL) techniques. A modular DRL architecture is proposed to obtain a route able to minimize the length of the paths, minimize the number of turns during the travel and select the ...
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps.
Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements
The context-dependence of extinction learning has been well studied and requires the hippocampus. However, the underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement learning and deep neural networks, we developed a m
In this work, a new deep reinforcement learning (DRL) based approach is proposed for automatic curve matching for well test interpretation, by using the double deep Q-network (DDQN). The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing...
Our proposed approach is therefore to obtain a set of intelligent agents that can bargain with each other in a coalitional bargaining game. To achieve a suitable level of agent intelligence, we train our agents using deep multi-agent reinforcement learning. A (transferable utility) coalitional game...