Deep Gaussian Process Proximal Policy Optimization
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915686749569024 |
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| author | van der Lende, Matthijs Cardenas-Cartagena, Juan |
| author_facet | van der Lende, Matthijs Cardenas-Cartagena, Juan |
| contents | Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural networks have enabled breakthroughs in RL, they often lack calibrated uncertainty estimates. We introduce Deep Gaussian Process Proximal Policy Optimization (GPPO), a scalable, model-free actor-critic algorithm that leverages Deep Gaussian Processes (DGPs) to approximate both the policy and value function. GPPO maintains competitive performance with respect to Proximal Policy Optimization on standard high-dimensional continuous control benchmarks while providing well-calibrated uncertainty estimates that can inform safer and more effective exploration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Gaussian Process Proximal Policy Optimization van der Lende, Matthijs Cardenas-Cartagena, Juan Machine Learning Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural networks have enabled breakthroughs in RL, they often lack calibrated uncertainty estimates. We introduce Deep Gaussian Process Proximal Policy Optimization (GPPO), a scalable, model-free actor-critic algorithm that leverages Deep Gaussian Processes (DGPs) to approximate both the policy and value function. GPPO maintains competitive performance with respect to Proximal Policy Optimization on standard high-dimensional continuous control benchmarks while providing well-calibrated uncertainty estimates that can inform safer and more effective exploration. |
| title | Deep Gaussian Process Proximal Policy Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.18214 |