Self-Composing Policies for Scalable Continual Reinforcement Learning
This work introduces a growable and modular neural network architecture that naturally avoids catastrophic forgetting and interference in con- tinual reinforcement learning. The structure of each module allows the selective combination of previous policies along with its internal policy, acceleratin...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/2042 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/2042 https://arxiv.org/abs/2506.14811v1 https://doi.org/10.48550/arXiv.2506.14811 |
| Access Level: | acceso abierto |
| Palabra clave: | Self-Composing Policies Scalable Continual Reinforcement |
| Sumario: | This work introduces a growable and modular neural network architecture that naturally avoids catastrophic forgetting and interference in con- tinual reinforcement learning. The structure of each module allows the selective combination of previous policies along with its internal policy, accelerating the learning process on the current task. Unlike previous growing neural network approaches, we show that the number of parame- ters of the proposed approach grows linearly with respect to the number of tasks, and does not sac- rifice plasticity to scale. Experiments conducted in benchmark continuous control and visual prob- lems reveal that the proposed approach achieves greater knowledge transfer and performance than alternative methods |
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