In value-based deep reinforcement learning, a pruned network is a good network
Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro
2/19/2024
cs.LGcs.AI
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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.
