Hierarchical reinforcement learning with central pattern generator for enabling a quadruped robot simulator to walk on a variety of terrains
Abstract We present a data-driven deep reinforcement learning (DRL) method for the optimization of a hierarchically structured control policy that includes the central pattern generator. This method, which is as a whole referred to as the hierarchical reinforcement learning with the central pattern...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-94163-2 |
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| Summary: | Abstract We present a data-driven deep reinforcement learning (DRL) method for the optimization of a hierarchically structured control policy that includes the central pattern generator. This method, which is as a whole referred to as the hierarchical reinforcement learning with the central pattern generator (HRL-CPG), is then evaluated with the expectation of its applicability in real robot controls. We observed that stable gait motions were gained in a reasonably small number of trials and errors. Thus, it can be deduced that our HRL-CPG can be a candidate DRL method that enables dynamical systems such as real or realistic robots to adapt to a variety of environments within a moderate physical time. |
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| ISSN: | 2045-2322 |