Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction
Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gawali, Mahendra Bhatu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea - Wang, Jiliang ELSEVIER, 2018, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:220 ; year:2021 ; day:23 ; month:05 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.knosys.2021.106945 |
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| 245 | 1 | 0 | |a Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction |
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| 520 | |a Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. | ||
| 520 | |a Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. | ||
| 650 | 7 | |a Artificial Neural Network |2 Elsevier | |
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| 650 | 7 | |a Root mean squared error |2 Elsevier | |
| 650 | 7 | |a Robotic arm movement |2 Elsevier | |
| 650 | 7 | |a Chicken Swarm-based Deer Hunting Optimization Algorithm |2 Elsevier | |
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10.1016/j.knosys.2021.106945 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001348.pica (DE-627)ELV05358161X (ELSEVIER)S0950-7051(21)00208-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Gawali, Mahendra Bhatu verfasserin aut Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Artificial Neural Network Elsevier Skill transfer knowledge Elsevier Reward maximization Elsevier Reinforcement Learning Elsevier Root mean squared error Elsevier Robotic arm movement Elsevier Chicken Swarm-based Deer Hunting Optimization Algorithm Elsevier Gawali, Swapnali Sunil oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:220 year:2021 day:23 month:05 pages:0 https://doi.org/10.1016/j.knosys.2021.106945 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 220 2021 23 0523 0 |
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10.1016/j.knosys.2021.106945 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001348.pica (DE-627)ELV05358161X (ELSEVIER)S0950-7051(21)00208-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Gawali, Mahendra Bhatu verfasserin aut Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Artificial Neural Network Elsevier Skill transfer knowledge Elsevier Reward maximization Elsevier Reinforcement Learning Elsevier Root mean squared error Elsevier Robotic arm movement Elsevier Chicken Swarm-based Deer Hunting Optimization Algorithm Elsevier Gawali, Swapnali Sunil oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:220 year:2021 day:23 month:05 pages:0 https://doi.org/10.1016/j.knosys.2021.106945 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 220 2021 23 0523 0 |
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10.1016/j.knosys.2021.106945 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001348.pica (DE-627)ELV05358161X (ELSEVIER)S0950-7051(21)00208-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Gawali, Mahendra Bhatu verfasserin aut Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Artificial Neural Network Elsevier Skill transfer knowledge Elsevier Reward maximization Elsevier Reinforcement Learning Elsevier Root mean squared error Elsevier Robotic arm movement Elsevier Chicken Swarm-based Deer Hunting Optimization Algorithm Elsevier Gawali, Swapnali Sunil oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:220 year:2021 day:23 month:05 pages:0 https://doi.org/10.1016/j.knosys.2021.106945 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 220 2021 23 0523 0 |
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10.1016/j.knosys.2021.106945 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001348.pica (DE-627)ELV05358161X (ELSEVIER)S0950-7051(21)00208-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Gawali, Mahendra Bhatu verfasserin aut Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Artificial Neural Network Elsevier Skill transfer knowledge Elsevier Reward maximization Elsevier Reinforcement Learning Elsevier Root mean squared error Elsevier Robotic arm movement Elsevier Chicken Swarm-based Deer Hunting Optimization Algorithm Elsevier Gawali, Swapnali Sunil oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:220 year:2021 day:23 month:05 pages:0 https://doi.org/10.1016/j.knosys.2021.106945 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 220 2021 23 0523 0 |
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10.1016/j.knosys.2021.106945 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001348.pica (DE-627)ELV05358161X (ELSEVIER)S0950-7051(21)00208-2 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Gawali, Mahendra Bhatu verfasserin aut Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. Artificial Neural Network Elsevier Skill transfer knowledge Elsevier Reward maximization Elsevier Reinforcement Learning Elsevier Root mean squared error Elsevier Robotic arm movement Elsevier Chicken Swarm-based Deer Hunting Optimization Algorithm Elsevier Gawali, Swapnali Sunil oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:220 year:2021 day:23 month:05 pages:0 https://doi.org/10.1016/j.knosys.2021.106945 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 220 2021 23 0523 0 |
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Optimized skill knowledge transfer model using hybrid Chicken Swarm plus Deer Hunting Optimization for human to robot interaction |
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Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. |
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Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. |
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Robots are more competent for progressing knowledge and learning new tasks that are of demanding interest. Service robots need trouble-free programming techniques facilitating the inexperienced human user to easily incorporate motion and perception skills or complex problem-solving strategies. The modern advancement in knowledge offers the most potent solution to this problem, enabling robots to incite enhanced skill knowledge transferability. This paper tempts to develop an efficient skill knowledge transfer between humans and computers using Transfer Expert Reinforcement Learning. Here, the movement of the robotic arm is considered which is solved with the assistance of one of the basic machine learning paradigms termed as Reinforcement Learning. As an improvement, the action features of Reinforcement Learning are optimized by a hybrid meta-heuristic algorithm with the integration of the Deer Hunting Optimization Algorithm and Chicken Swarm Optimization termed as Chicken Swarm-plus Deer Hunting Optimization Algorithm. Moreover, the Artificial Neural Network plays a major role here to determine the desired movement based on the input kinematic movements. The main objective of optimized Reinforcement Learning is to maximize the reward, which in turn minimizes the error difference between the desired and the predicted movement. The experimental analysis of the Chicken Swarm-plus Deer Hunting Optimization Algorithm based Reinforcement Learning over the conventional and other heuristic-based Reinforcement Learning proves the effective performance of the proposed model. |
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