Hierarchical Orchestra of Policies
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910685655465984 |
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| author | Cannon, Thomas P Simsek, Özgür |
| author_facet | Cannon, Thomas P Simsek, Özgür |
| contents | Continual reinforcement learning poses a major challenge due to the tendency of agents to experience catastrophic forgetting when learning sequential tasks. In this paper, we introduce a modularity-based approach, called Hierarchical Orchestra of Policies (HOP), designed to mitigate catastrophic forgetting in lifelong reinforcement learning. HOP dynamically forms a hierarchy of policies based on a similarity metric between the current observations and previously encountered observations in successful tasks. Unlike other state-of-the-art methods, HOP does not require task labelling, allowing for robust adaptation in environments where boundaries between tasks are ambiguous. Our experiments, conducted across multiple tasks in a procedurally generated suite of environments, demonstrate that HOP significantly outperforms baseline methods in retaining knowledge across tasks and performs comparably to state-of-the-art transfer methods that require task labelling. Moreover, HOP achieves this without compromising performance when tasks remain constant, highlighting its versatility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Orchestra of Policies Cannon, Thomas P Simsek, Özgür Machine Learning Artificial Intelligence Continual reinforcement learning poses a major challenge due to the tendency of agents to experience catastrophic forgetting when learning sequential tasks. In this paper, we introduce a modularity-based approach, called Hierarchical Orchestra of Policies (HOP), designed to mitigate catastrophic forgetting in lifelong reinforcement learning. HOP dynamically forms a hierarchy of policies based on a similarity metric between the current observations and previously encountered observations in successful tasks. Unlike other state-of-the-art methods, HOP does not require task labelling, allowing for robust adaptation in environments where boundaries between tasks are ambiguous. Our experiments, conducted across multiple tasks in a procedurally generated suite of environments, demonstrate that HOP significantly outperforms baseline methods in retaining knowledge across tasks and performs comparably to state-of-the-art transfer methods that require task labelling. Moreover, HOP achieves this without compromising performance when tasks remain constant, highlighting its versatility. |
| title | Hierarchical Orchestra of Policies |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.03008 |