title: In deep reinforcement learning, a pruned network is a good network
summary: 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.
paper_authors._id: 65d5fba1900872477fc136b0
paper_authors.name: Johan Obando-Ceron
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-21T16:29:04.482Z
paper_authors._id: 6478f30ea68454566353ef95
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6478f30ea68454566353ef95/hX5NiVPyQbK8TIBKnT4J1.jpeg
paper_authors.fullname: Johan Samir Obando Ceron
paper_authors.type: user
paper_authors.user: johanobandoc
paper_authors._id: 65d5fba1900872477fc136b1
paper_authors.name: Aaron Courville
paper_authors._id: 65d5fba1900872477fc136b2
paper_authors.name: Pablo Samuel Castro
paper_title: In deep reinforcement learning, a pruned network is a good network
paper_summary: 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.
paper_ai_summary: Gradual magnitude pruning in deep reinforcement learning enhances parameter effectiveness, improving performance and achieving scaling law efficiency.
paper_ai_keywords: sparse training magnitude pruning parameter effectiveness scaling law