Actor-Critic Control with Reference Model Learning

Grondman, I.; Busoniu, Maarten Vaandrager Lucian; Babuška, Robert; Schuitema, E.

doi:10.3182/20110828-6-it-1002.00759

Cited by 13 publications

(3 citation statements)

References 15 publications

(9 reference statements)

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“…where Q υ ðs, aÞ is state-action value function and transition function is fs t , a t , r t+1 , s t+1 , a t+1 g. By using TD error, the critic parameter v can be updated by the gradientdescent update rule [30] which is represented in the 7 Wireless Communications and Mobile Computing following equation:…”

Section: Actor Part Policy Gradient (Pg) Methodsmentioning

confidence: 99%

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Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Pradhan

Bisoy

Sain

2022

Wireless Communications and Mobile Computing

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Increasing scale of task in cloud network leads to problem in load balancing and its improvement in parameters. In this paper, we proposed a hybrid scheduling policy which is hybrid of both Particle Swarm Optimization (PSO) algorithm and actor-critic algorithm named as Hybrid Particle Swarm Optimization Actor Critic (HPSOAC) to solve this issue. This hybrid scheduling policy helps to each agent to improve an individual learning as well as learning through exchanging information among other agents. An experiment is carried out by the help of Python simulator with TensorFlow. Outcome shows that our proposed scheduling policy reduces 5.16% and 10.86% in energy consumption, reduces 7.13% and 10.04% in makespan time, and has marginally better resource utilization over Deep Q-network (DQN) and Q-learning based on Modified Particle Swarm Optimization (QMPSO) algorithm, respectively.

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Section: Actor Part Policy Gradient (Pg) Methodsmentioning

confidence: 99%

“…In cloud computing, all incoming tasks should be allocated to suitable VMs for executing in minimum time. It depends upon the capacity of the VM that is represented in Equation (30). Load of the VM is calculated in Equation (31).…”

Section: Hpsoac Scheduling Policy For Parameter Updatingmentioning

confidence: 99%

Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Pradhan

Bisoy

Sain

2022

Wireless Communications and Mobile Computing

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“…Recent studies show that RL can calculate the optimal impedance model time-varying environments (Wang et al , 2015). The optimal critical algorithm (Grondman et al , 2012a, 2012b, 2011) is applied to obtain the optimum impedance strength. The parameters of the impedance model are estimated by the exponential weighted minimum square (Astrom and Wittenmark, 1989), but it needs a parameterization model for the impedance parameters (Chih and Huang, 2004).…”

Section: Introductionmentioning

confidence: 99%

Position/force control of robot manipulators using reinforcement learning

Perrusquía

Soria

2019

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Purpose The position/force control of the robot needs the parameters of the impedance model and generates the desired position from the contact force in the environment. When the environment is unknown, learning algorithms are needed to estimate both the desired force and the parameters of the impedance model. Design/methodology/approach In this paper, the authors use reinforcement learning to learn only the desired force, then they use proportional-integral-derivative admittance control to generate the desired position. The results of the experiment are presented to verify their approach. Findings The position error is minimized without knowing the environment or the impedance parameters. Another advantage of this simplified position/force control is that the transformation of the Cartesian space to the joint space by inverse kinematics is avoided by the feedback control mechanism. The stability of the closed-loop system is proven. Originality/value The position error is minimized without knowing the environment or the impedance parameters. The stability of the closed-loop system is proven.

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Robot ℋ2 Neural Control Using Reinforcement Learning

Yu¹,

Perrusquía²

2021

Human‐Robot Interaction Control Using Reinforcement Learning

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Actor-Critic Control with Reference Model Learning

Cited by 13 publications

References 15 publications

Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Position/force control of robot manipulators using reinforcement learning

Robot ℋ2 Neural Control Using Reinforcement Learning

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