In 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 agents to maximize parameter effectiveness. This
results in networks that yield dramatic performance improvements over
traditional networks and exhibit a type of "scaling law", using only a small
fraction of the full network parameters.
65d5fba1900872477fc136b0
Johan Obando-Ceron
claimed_verified
2024-02-21T16:29:04.482Z
6478f30ea68454566353ef95
https://cdn-avatars.huggingface.co/v1/production/uploads/6478f30ea68454566353ef95/hX5NiVPyQbK8TIBKnT4J1.jpeg
Johan Samir Obando Ceron
user
johanobandoc
65d5fba1900872477fc136b1
Aaron Courville
65d5fba1900872477fc136b2
Pablo Samuel Castro
In 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 agents to maximize parameter effectiveness. This
results in networks that yield dramatic performance improvements over
traditional networks and exhibit a type of "scaling law", using only a small
fraction of the full network parameters.
Gradual magnitude pruning in deep reinforcement learning enhances parameter effectiveness, improving performance and achieving scaling law efficiency.
sparse training magnitude pruning parameter effectiveness scaling law