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arXiv · 2402.12479

In value-based deep reinforcement learning, a pruned network is a good network

Johan Obando-Ceron·Aaron Courville·Pablo Samuel Castro
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Abstract

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.

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Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro. 2024-02-19. In value-based deep reinforcement learning, a pruned network is a good network. https://arxiv.org/abs/2402.12479

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