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actor-critic
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Application of actor-critic reinforcement learning method for control of a sagittal arm during oscillatory movement
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 16, Issue 6 , 2004 , Pages 305-312 ; 10162372 (ISSN) ; Lucas, C ; Parnianpour, M ; Sharif University of Technology
Institute of Biomedical Engineering
2004
Abstract
Numerous disciplines are engaged in studies involving motor control. In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve an oscillatory movement with variable amplitude and frequency. The system is highly nonlinear in all its physical and physiological attributes. The major physiological characteristics of this system are simultaneous activation of a pair of nonlinear muscle-like-actuators for control purposes, existence of nonlinear spindle-like sensors and Golgi tendon organ-like sensor, actions of gravity and external loading. Transmission delays are included in the afferent and efferent neural paths to account...
Neuromuscular control of sagittal ARM during repetitive movement by actor-critic reinforcement learning method
, Article Intelligent Automation and Control Trends, Principles, and Applications - International Symposium on Intelligent Automation and Control, ISIAC - Sixth Biannual World Automation Congress, WAC 2004, Seville, 28 June 2004 through 1 July 2004 ; 2004 , Pages 371-376 ; 1889335223 (ISBN) ; Lucas, C ; Parnianpour, M ; Sharif University of Technology
2004
Abstract
In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve an oscillatory movement with variable amplitude and frequency. This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by State-Action-Reward-State-Action (SARSA) method. The system showed excellent tracking capability and after 280 epochs the RMS error for position and velocity profiles were 0.02, 0.04 radian and radian/sec,...
The role of multisensor data fusion in neuromuscular control of a sagittal arm with a pair of muscles using actor-critic reinforcement learning method
, Article Technology and Health Care ; Volume 12, Issue 6 , 2004 , Pages 425-438 ; 09287329 (ISSN) ; Parnianpour, M ; Lucas, C ; Sharif University of Technology
IOS Press
2004
Abstract
In this study, we consider the role of multisensor data fusion in neuromuscular control using an actor-critic reinforcement learning method. The model we use is a single link system actuated by a pair of muscles that are excited with alpha and gamma signals. Various physiological sensor information such as proprioception, spindle sensors, and Golgi tendon organs have been integrated to achieve an oscillatory movement with variable amplitude and frequency, while achieving a stable movement with minimum metabolic cost and coactivation. The system is highly nonlinear in all its physical and physiological attributes. Transmission delays are included in the afferent and efferent neural paths to...
Actor-critic-based ink drop spread as an intelligent controller
, Article Turkish Journal of Electrical Engineering and Computer Sciences ; Volume 21, Issue 4 , 2013 , Pages 1015-1034 ; 13000632 (ISSN) ; Afrakoti, I. E. P ; Bagherishouraki, S ; Sharif University of Technology
2013
Abstract
This paper introduces an innovative adaptive controller based on the actor-critic method. The proposed approach employs the ink drop spread (IDS) method as its main engine. The IDS method is a new trend in softcomputing approaches that is a universal fuzzy modeling technique and has been also used as a supervised controller. Its process is very similar to the processing system of the human brain. The proposed actor-critic method uses an IDS structure as an actor and a 2-dimensional plane, representing control variable states, as a critic that estimates the lifetime goodness of each state. This method is fast, simple, and away from mathematical complexity. The proposed method uses the...
Real-Time IDS using reinforcement learning
, Article 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008, Shanghai, 21 December 2008 through 22 December 2008 ; Volume 2 , January , 2008 , Pages 593-597 ; 9780769534978 (ISBN) ; Bagheri Shouraki, S ; Hosein, K ; Mahdi, D ; Sharif University of Technology
2008
Abstract
In this paper we proposed a new real-time learning method. The engine of this method is a fuzzy-modeling technique which is called ink drop spread (IDS). IDS method has good convergence and is very simple and away from complex formula. The proposed method uses a reinforcement learning approach by an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the IDS by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. It is shown that the system can adapt itself, commencing with random actions. © 2008 IEEE
Optimal Process Planning for Automated Robotic Assembly of Mechanical Assembles based on Reinforcement Learning Method
, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
Nowadays, the assembly process is planned by an expert and requires knowledge and it is time-consuming. The flexibility and optimality of the assembly plan depend on the knowledge and creativity of the expert, and therefore expertise is an important parameter in developing the assembly plan. Therefore, the use of intelligent methods to plan the assembly process has been considered by many researchers. . The reinforcing learning approach has the potential to solve complex problems due to the use of experience gained from interacting with the environment and Has been successfully implemented in controlling many robotic tasks. However, due to the inherent complexity of the assembly, as well as...
Reinforcement learning based on active learning method
, Article Proceedings - 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008, 21 December 2008 through 22 December 2008, Shanghai ; Volume 2 , 2008 , Pages 598-602 ; 9780769534978 (ISBN) ; Bagheri Shouraki, S ; Khasteh, H ; Kiaei, A. A ; Sharif University of Technology
2008
Abstract
In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. The goodness of an action is modeled on Reward-Penalty-Plane. IDS planes will be updated according to this plane. It is shown that the...
Neuromuscular control of the point to point and oscillatory movements of a sagittal arm with the actor-critic reinforcement learning method
, Article Computer Methods in Biomechanics and Biomedical Engineering ; Volume 8, Issue 2 , 2005 , Pages 103-113 ; 10255842 (ISSN) ; Parnianpour, M ; Lucas, C ; Sharif University of Technology
2005
Abstract
In this study, we have used a single link system with a pair of muscles that are excited with alpha and gamma signals to achieve both point to point and oscillatory movements with variable amplitude and frequency. The system is highly nonlinear in all its physical and physiological attributes. The major physiological characteristics of this system are simultaneous activation of a pair of nonlinear musclelike- actuators for control purposes, existence of nonlinear spindle-like sensors and Golgi tendon organlike sensor, actions of gravity and external loading. Transmission delays are included in the afferent and efferent neural paths to account for a more accurate representation of the reflex...