Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these cha...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-0825/13/11/458 |
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| author | Liwei Mei Pengjie Xu |
| author_facet | Liwei Mei Pengjie Xu |
| author_sort | Liwei Mei |
| collection | DOAJ |
| description | Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these challenges, this manuscript pioneers a framework integrating zero-shot learning combined with hierarchical reinforcement learning to enhance agent decision-making in complex environments. Zero-shot learning enables agents to infer correct actions for previously unseen objects or situations based on learned semantic associations. Subsequently, the path planning component utilizes hierarchical reinforcement learning with adaptive replay buffer, directed by the insights gained from zero-shot learning, to make decisions effectively. Two parts are trained separately, so zero-shot learning is available in different and unseen environments. Through simulation experiments, we compare the traditional hierarchical reinforcement learning method with the proposed method. The results prove that this structure can make full use of environmental information to generalize across unseen environments and plan collision-free paths. |
| format | Article |
| id | doaj-art-14821b1a5a724f839fc0515a3d001359 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-14821b1a5a724f839fc0515a3d0013592024-11-26T17:42:31ZengMDPI AGActuators2076-08252024-11-01131145810.3390/act13110458Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel EnvironmentsLiwei Mei0Pengjie Xu1School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, ChinaPath planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these challenges, this manuscript pioneers a framework integrating zero-shot learning combined with hierarchical reinforcement learning to enhance agent decision-making in complex environments. Zero-shot learning enables agents to infer correct actions for previously unseen objects or situations based on learned semantic associations. Subsequently, the path planning component utilizes hierarchical reinforcement learning with adaptive replay buffer, directed by the insights gained from zero-shot learning, to make decisions effectively. Two parts are trained separately, so zero-shot learning is available in different and unseen environments. Through simulation experiments, we compare the traditional hierarchical reinforcement learning method with the proposed method. The results prove that this structure can make full use of environmental information to generalize across unseen environments and plan collision-free paths.https://www.mdpi.com/2076-0825/13/11/458path planningzero-shot learninghierarchical reinforcement learningadaptive agents |
| spellingShingle | Liwei Mei Pengjie Xu Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments Actuators path planning zero-shot learning hierarchical reinforcement learning adaptive agents |
| title | Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments |
| title_full | Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments |
| title_fullStr | Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments |
| title_full_unstemmed | Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments |
| title_short | Path Planning for Robots Combined with Zero-Shot and Hierarchical Reinforcement Learning in Novel Environments |
| title_sort | path planning for robots combined with zero shot and hierarchical reinforcement learning in novel environments |
| topic | path planning zero-shot learning hierarchical reinforcement learning adaptive agents |
| url | https://www.mdpi.com/2076-0825/13/11/458 |
| work_keys_str_mv | AT liweimei pathplanningforrobotscombinedwithzeroshotandhierarchicalreinforcementlearninginnovelenvironments AT pengjiexu pathplanningforrobotscombinedwithzeroshotandhierarchicalreinforcementlearninginnovelenvironments |