We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| tinkoff-ai/lb-sac | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| generate_sweep_commands | Ran | tinkoff-ai/lb-sac/sweep.py code served (permissive licence) · get_code("40e41b121401a1ec") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks. To address this problem, large-batch optimization was introduced. This approach demonstrated that scaling mini-batch sizes with appropriate learning rate adjustments can speed up the training process by orders of magnitude. While long training time was not typically a major issue for model-free deep offline RL algorithms, recently introduced Q-ensemble methods achieving state-of-the-art performance made this issue more relevant, notably extending the training duration. In this work, we demonstrate how this class of methods can benefit from large-batch optimization, which is commonly overlooked by the deep offline RL community. We show that scaling the mini-batch size and naively adjusting the learning rate allows for (1) a reduced size of the Q-ensemble, (2) stronger penalization of out-of-distribution actions, and (3) improved convergence time, effectively shortening training duration by 3-4x times on average.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.11092")
get_code_for_paper("2211.11092")
have("2211.11092")
Connect an agent — have() is free.