Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909607704657920 |
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| author | Choe, Jean Seong Bjorn Choi, Bumkyu Kim, Jong-kook |
| author_facet | Choe, Jean Seong Bjorn Choi, Bumkyu Kim, Jong-kook |
| contents | This report presents our reinforcement learning-based approach for the swing-up and stabilisation tasks of the acrobot and pendubot, tailored specifcially to the updated guidelines of the 3rd AI Olympics at ICRA 2025. Building upon our previously developed Average-Reward Entropy Advantage Policy Optimization (AR-EAPO) algorithm, we refined our solution to effectively address the new competition scenarios and evaluation metrics. Extensive simulations validate that our controller robustly manages these revised tasks, demonstrating adaptability and effectiveness within the updated framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks Choe, Jean Seong Bjorn Choi, Bumkyu Kim, Jong-kook Robotics This report presents our reinforcement learning-based approach for the swing-up and stabilisation tasks of the acrobot and pendubot, tailored specifcially to the updated guidelines of the 3rd AI Olympics at ICRA 2025. Building upon our previously developed Average-Reward Entropy Advantage Policy Optimization (AR-EAPO) algorithm, we refined our solution to effectively address the new competition scenarios and evaluation metrics. Extensive simulations validate that our controller robustly manages these revised tasks, demonstrating adaptability and effectiveness within the updated framework. |
| title | Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.07516 |