Simulation Analysis of a Deep Reinforcement Learning Approach for Task Selection by Autonomous Material Handling Vehicles

Li, Maojia Patrick; Sankaran, Prashant; Kühl, Michael; Ganguly, Amlan; Kwasinski, Alexis; Ptucha, Raymond

doi:10.1109/wsc.2018.8632448

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…Simulation results validated the effectiveness of the proposed activation function and spreading sequence in automated policy selection. The combination also expanded to address specific tasks within warehouses with Li et al [18], which focused on task selection in material handling and presented a deep reinforcement learning (DRL) methodology for autonomous vehicles in a warehouse context. The authors conducted simulation-based experiments to train and evaluate the capabilities of the proposed method.…”

Section: Applications Of Rl With Simulation In Warehouse Operationsmentioning

confidence: 99%

“…The reasons are obviously the increased complexity of handling the inter-software communication and the potentially richer environment from which the agent needs to learn, as exemplified in the present work with FlexSim. Drakaki and Tzionas [16] Kono et al [17] Li et al [18] Sartoretti et al [19] Li et al [20] Barat et al [21] Sun and Li [22] Xiao et al [23] Yang et al [24] Ushida et al [25] Shen et al [26] Newaz and Alam [27] CoppeliaSim Peyas et al [28] Ha et al [29] Liu et al [30] Ushida et al [31] Lee and Jeong [32] Tang et al [33] Li et al [34] CloudSim…”

Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Leon,

Li,

Martin

et al. 2023

Algorithms

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The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator.

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Section: Applications Of Rl With Simulation In Warehouse Operationsmentioning

confidence: 99%

Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Leon,

Li,

Martin

et al. 2023

Algorithms

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“…Q-learning is a model-free algorithm for RL to learn the Q function without knowing a model of the environment (e.g., the state transition probabilities). Watkins & Dayan (1992) present in detail that Q-learning converges to optimal action values based on his previous work (Watkins (1989)) In recent years, a large number of applications of reinforcement learning exist in logistics and transportation related fields, e.g., manufacturing planning for material handling systems (Govindaiah & Petty (2019), Li et al (2018)), routing in baggage handling systems (Mukhutdinov et al (2019)), order dispatching in ridehailing/ride-sharing systems (Xu et al (2018), Tang et al (2019)), rebalancing in bike-sharing system (Pan et al (2019)). Reinforcement learning also shows promising potential for solving classical combinatorial optimization problems, e.g., travelling salesman problem (Vinyals et al (2015)), vehicle routing problems (Kool et al (2019), Nazari et al (2018)) etc.…”

Section: Reinforcement Learning and Its Applicationsmentioning

confidence: 99%

Reinforcement learning for multi-item retrieval in the puzzle-based storage system

He¹,

Liu²,

Duan³

et al. 2022

Preprint

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Nowadays, fast delivery services have created the need for high-density warehouses. The puzzle-based storage system is a practical way to enhance the storage density, however, facing difficulties in the retrieval process. In this work, a deep reinforcement learning algorithm, specifically the Double&Dueling Deep Q Network, is developed to solve the multi-item retrieval problem in the system with general settings, where multiple desired items, escorts, and I/O points are placed randomly. Additionally, we propose a general compact integer programming model to evaluate the solution quality. Extensive numerical experiments demonstrate that the reinforcement learning approach can yield high-quality solutions and outperforms three related state-of-theart heuristic algorithms. Furthermore, a conversion algorithm and a decomposition framework are proposed to handle simultaneous movement and large-scale instances respectively, thus improving the applicability of the PBS system.

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“…Based on this context [34] propose the use of Deep-RL to perform autonomous mapless navigation for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs), robots that can operate in both, air or water media. Other influential work includes [35].…”

Section: Introductionmentioning

confidence: 99%

Navigation of Autonomous Vehicles using Reinforcement Learning with Generalized Advantage Estimation

Jacinto¹,

Martínez²,

Sarmiento³

2023

IJACSA

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This study proposes a reinforcement learning approach using Generalized Advantage Estimation (GAE) for autonomous vehicle navigation in complex environments. The method is based on the actor-critic framework, where the actor network predicts actions and the critic network estimates state values. GAE is used to compute the advantage of each action, which is then used to update the actor and critic networks. The approach was evaluated in a simulation of an autonomous vehicle navigating through challenging environments and it was found to effectively learn and improve navigation performance over time. The results suggest GAE as a promising direction for further research in autonomous vehicle navigation in complex environments.

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Simulation Analysis of a Deep Reinforcement Learning Approach for Task Selection by Autonomous Material Handling Vehicles

Cited by 11 publications

References 14 publications

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Reinforcement learning for multi-item retrieval in the puzzle-based storage system

Navigation of Autonomous Vehicles using Reinforcement Learning with Generalized Advantage Estimation

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