A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning
Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of “curse of dimensionality,” which means that the st...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/120760 |
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| author | Yuchen Fu Quan Liu Xionghong Ling Zhiming Cui |
| author_facet | Yuchen Fu Quan Liu Xionghong Ling Zhiming Cui |
| author_sort | Yuchen Fu |
| collection | DOAJ |
| description | Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of “curse of dimensionality,” which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The “curse of dimensionality” problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well. |
| format | Article |
| id | doaj-art-b5cd1ef362ba4bef85b26678258514d6 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-b5cd1ef362ba4bef85b26678258514d62025-02-03T06:07:21ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/120760120760A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement LearningYuchen Fu0Quan Liu1Xionghong Ling2Zhiming Cui3Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, Jiangsu 215123, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, ChinaSchool of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, ChinaReinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of “curse of dimensionality,” which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The “curse of dimensionality” problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.http://dx.doi.org/10.1155/2014/120760 |
| spellingShingle | Yuchen Fu Quan Liu Xionghong Ling Zhiming Cui A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning The Scientific World Journal |
| title | A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning |
| title_full | A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning |
| title_fullStr | A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning |
| title_full_unstemmed | A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning |
| title_short | A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning |
| title_sort | reward optimization method based on action subrewards in hierarchical reinforcement learning |
| url | http://dx.doi.org/10.1155/2014/120760 |
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