Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning

Kono, Hitoshi; Katayama, Ren; Takakuwa, Yusaku; Wen, Wen; Suzuki, Takahiro

doi:10.14569/ijacsa.2019.0101202

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…Kono et al [17] introduced an automatic policy selection method for transfer learning in RL, based on spreading activation theory. The proposed method enhanced the adaptability of RL algorithms in transfer learning.…”

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%

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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

View full text Add to dashboard Cite

show abstract

“…Kono et al are proposed transfer reinforcement learning with spreading activation model which is called SAP-net to select the policies adaptivelly according to environments [7]. SAP-net is discussed effectiveness by simplified computer simulation such as shortest path problem, and it is defined theoretically for implementation.…”

Section: A Spreading Activation and Existing Researchmentioning

confidence: 99%

“…As a function approximation with reinforcement learning method, Deep Q-Network is implemented in this study. Therefore proposed method is based on policy selection method in transfer reinforcement learning with spreading activation model proposed by Kono et al [7], and is tuned that the policy selection method can be implemented by Deep Q-Network as a part of reinforcement learning algorithm. In the experiment, CartPole and MountainCar are adopted as evaluation function, which are classical reinforcement learning tasks.…”

Section: The Principal Aim Of This Studymentioning

confidence: 99%

“…For the above problems, in order to flexibly select a policy, we apply the spreading activation model as a psychology model that aids human behavior and judgment in the proposed policy selection method based on previous method [7]. In addition, Deep Q-Network method is adopted as function approximation of policy of reinforcement learning, and proposed method is evaluated with various classical reinforcement learning tasks in computer simulation.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Reusing Policy Selection using Spreading Activation Model in Deep Reinforcement Learning

Takakuwa¹,

Kono²,

Fujii³

et al. 2021

IJACSA

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This paper describes a policy transfer method of a reinforcement learning agent based on the spreading activation model of cognitive psychology. This method has a prospect of increasing the possibility of policy reuse, adapting to multiple tasks, and assessing agent mechanism differences. In the existing methods, policies are evaluated and manually selected depending on the target-task. The proposed method generates a policy network that calculates the relevance between policies in order to select and transfer a specific policy that is presumed to be effective based on the current situation of the agent while learning. Using a policy network graph structure, the proposed method decides the most effective policy while repeating probabilistic selection, activation, and spread processing. In the experiment section, this study describes experiments conducted to evaluate usefulness, conditions of use, and the usable range of the proposed method. Tests using CartPole and MountainCar, which are classical reinforcement learning tasks, are described and transfer learning is compared between the proposed method and a Deep Q-Network without transfer. As the experimental results, usefulness was suggested in the transfer learning of the same task without manual compared with previous method with various conditions.

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Body Calibration: Automatic Inter-Task Mapping between Multi-Legged Robots with Different Embodiments in Transfer Reinforcement Learning

Ikeda

Kono

Watanabe³

et al. 2022

Actuators

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Machine learning algorithms are effective in realizing the programming of robots that behave autonomously for various tasks. For example, reinforcement learning (RL) does not require supervision or data sets; the RL agent explores solutions by itself. However, RL requires a long learning time, particularly for actual robot learning situations. Transfer learning (TL) in RL has been proposed to address this limitation. TL realizes fast adaptation and decreases the problem-solving time by utilizing the knowledge of the policy, value function, and Q-function from RL. Taylor proposed TL using inter-task mapping that defines the correspondence between the state and action between the source and target domains. Inter-task mapping is defined based on human intuition and experience; therefore, the effect of TL may not be obtained. The difference in robot shapes for TL is similar to the cognition in the modification of human body composition, and automatic inter-task mapping can be performed by referring to the body representation that is assumed to be stored in the human brain. In this paper, body calibration is proposed, which refers to the physical expression in the human brain. It realizes automatic inter-task mapping by acquiring data modeled on a body diagram that illustrates human body composition and posture. The proposed method is evaluated in a TL situation from a computer simulation of RL to actual robot control with a multi-legged robot.

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Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning

Cited by 3 publications

References 20 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

Autonomous Reusing Policy Selection using Spreading Activation Model in Deep Reinforcement Learning

Body Calibration: Automatic Inter-Task Mapping between Multi-Legged Robots with Different Embodiments in Transfer Reinforcement Learning

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