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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.24016 |
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| _version_ | 1866929570334113792 |
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| author | Crowder, Douglas C. Trappett, Matthew L. McKenzie, Darrien M. Chance, Frances S. |
| author_facet | Crowder, Douglas C. Trappett, Matthew L. McKenzie, Darrien M. Chance, Frances S. |
| contents | Hindsight experience replay (HER) is well-known to accelerate goal-based reinforcement learning (RL). While HER is generally applied to off-policy RL algorithms, we previously showed that HER can also accelerate on-policy algorithms, such as proximal policy optimization (PPO), for goal-based Predator-Prey environments. Here, we show that we can improve the previous PPO-HER algorithm by selectively applying HER in a principled manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_24016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maximum Entropy Hindsight Experience Replay Crowder, Douglas C. Trappett, Matthew L. McKenzie, Darrien M. Chance, Frances S. Machine Learning Hindsight experience replay (HER) is well-known to accelerate goal-based reinforcement learning (RL). While HER is generally applied to off-policy RL algorithms, we previously showed that HER can also accelerate on-policy algorithms, such as proximal policy optimization (PPO), for goal-based Predator-Prey environments. Here, we show that we can improve the previous PPO-HER algorithm by selectively applying HER in a principled manner. |
| title | Maximum Entropy Hindsight Experience Replay |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.24016 |