Adaptive Ensemble Aggregation for Actor-Critics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914532979376128 |
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| author | Werge, Nicklas Wu, Yi-Shan Haussmann, Manuel Tasdighi, Bahareh Kandemir, Melih |
| author_facet | Werge, Nicklas Wu, Yi-Shan Haussmann, Manuel Tasdighi, Bahareh Kandemir, Melih |
| contents | Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or task-specific hyperparameters to balance overestimation bias and variance, leaving the challenge of a truly adaptive approach open. We introduce Adaptive Ensemble Aggregation (AEA), an algorithm that dynamically constructs ensemble-based targets for both critic and actor updates directly from training dynamics. We prove that AEA converges to a unique equilibrium where the aggregation parameter minimizes value estimation error within a defined stability region. Theoretically, we establish that AEA achieves a shrinkage property where the estimation bias vanishes as the total ensemble size grows. Unlike subset-based methods like REDQ, which hit an information bottleneck determined by a fixed variance floor regardless of the ensemble size, AEA exploits the full ensemble to achieve optimal variance reduction-scaling inversely with the total number of models-and maximal Fisher information. Furthermore, we provide a formal guarantee for monotonic policy improvement under this adaptive regime. Extensive evaluations on various continuous control tasks demonstrate that AEA outperforms, on the majority of tasks, state-of-the-art baselines, providing a robust and self-calibrating framework for ensemble-based reinforcement learning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_23501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Ensemble Aggregation for Actor-Critics Werge, Nicklas Wu, Yi-Shan Haussmann, Manuel Tasdighi, Bahareh Kandemir, Melih Machine Learning Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or task-specific hyperparameters to balance overestimation bias and variance, leaving the challenge of a truly adaptive approach open. We introduce Adaptive Ensemble Aggregation (AEA), an algorithm that dynamically constructs ensemble-based targets for both critic and actor updates directly from training dynamics. We prove that AEA converges to a unique equilibrium where the aggregation parameter minimizes value estimation error within a defined stability region. Theoretically, we establish that AEA achieves a shrinkage property where the estimation bias vanishes as the total ensemble size grows. Unlike subset-based methods like REDQ, which hit an information bottleneck determined by a fixed variance floor regardless of the ensemble size, AEA exploits the full ensemble to achieve optimal variance reduction-scaling inversely with the total number of models-and maximal Fisher information. Furthermore, we provide a formal guarantee for monotonic policy improvement under this adaptive regime. Extensive evaluations on various continuous control tasks demonstrate that AEA outperforms, on the majority of tasks, state-of-the-art baselines, providing a robust and self-calibrating framework for ensemble-based reinforcement learning. |
| title | Adaptive Ensemble Aggregation for Actor-Critics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.23501 |