We lifted 7 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Z-T-WANG/LaProp-Optimizer | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| beta_scheduler | Ran | Z-T-WANG/LaProp-Optimizer/rainbow/common/utils.py code served (permissive licence) · get_code("11fc51b3483204eb") |
| create_log_dir | Ran | Z-T-WANG/LaProp-Optimizer/rainbow/common/utils.py code served (permissive licence) · get_code("878f223c996c5522") |
| epsilon_scheduler | Ran | Z-T-WANG/LaProp-Optimizer/rainbow/common/utils.py code served (permissive licence) · get_code("2e0c79ff61cfd9ae") |
| make_atari | Ran | Z-T-WANG/LaProp-Optimizer/rainbow/common/wrappers.py code served (permissive licence) · get_code("6eb2fa89d6e4a36b") |
| wrap_deepmind | Ran | Z-T-WANG/LaProp-Optimizer/rainbow/common/wrappers.py code served (permissive licence) · get_code("34023060eaec34a2") |
| wrap_pytorch | Ran | Z-T-WANG/LaProp-Optimizer/rainbow/common/wrappers.py code served (permissive licence) · get_code("d818d11d4b5f6381") |
| DQN | Not yet run | Z-T-WANG/LaProp-Optimizer/rainbow/model.py code served (permissive licence) · get_code("ed2dc93a9723ec69") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We identity a by-far-unrecognized problem of Adam-style optimizers which results from unnecessary coupling between momentum and adaptivity. The coupling leads to instability and divergence when the momentum and adaptivity parameters are mismatched. In this work, we propose a method, Laprop, which decouples momentum and adaptivity in the Adam-style methods. We show that the decoupling leads to greater flexibility in the hyperparameters and allows for a straightforward interpolation between the signed gradient methods and the adaptive gradient methods. We experimentally show that Laprop has consistently improved speed and stability over Adam on a variety of tasks. We also bound the regret of Laprop on a convex problem and show that our bound differs from that of Adam by a key factor, which demonstrates its advantage.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.04839")
get_code_for_paper("2002.04839")
have("2002.04839")
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