In value-based deep reinforcement learning, a pruned network is a good network
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909230892580864 |
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| author | Obando-Ceron, Johan Courville, Aaron Castro, Pablo Samuel |
| author_facet | Obando-Ceron, Johan Courville, Aaron Castro, Pablo Samuel |
| contents | Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables value-based agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12479 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | In value-based deep reinforcement learning, a pruned network is a good network Obando-Ceron, Johan Courville, Aaron Castro, Pablo Samuel Machine Learning Artificial Intelligence Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage prior insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables value-based agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters. |
| title | In value-based deep reinforcement learning, a pruned network is a good network |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.12479 |