In value-based deep reinforcement learning, a pruned network is a good network
<|reference_start|>In value-based deep reinforcement learning, a pruned network is a good network: 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.<|reference_end|>
@article{obando-ceron2024in,
    title={In value-based deep reinforcement learning, a pruned network is a good
  network},
    author={Johan Obando-Ceron and Aaron Courville and Pablo Samuel Castro},
    journal={arXiv preprint arXiv:2402.12479},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12479},
    primaryClass={cs.LG cs.AI}
}