Doina Precup, Emmanuel Bengio, Ling Pan, Elaine Lau, Stephen Zhewen Lu
We lifted 8 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| yunglau/QGFN | canonical | 6 of 7 |
| yunglau/qgfn | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| aggregate_iqm | Ran | yunglau/QGFN/utils/metrics.py code served (permissive licence) · get_code("42c82ab33c19b894") |
| get_groupby_value | Ran | yunglau/QGFN/utils/metrics.py code served (permissive licence) · get_code("31cec085da1cc1c5") |
| make_sh_script | Ran | yunglau/QGFN/utils/runs.py code served (permissive licence) · get_code("5dc95db4192ee1e6") |
| mean_confidence_interval | Ran | yunglau/QGFN/utils/metrics.py code served (permissive licence) · get_code("40f2f8185e2b1e3f") |
| scheduler | Ran | yunglau/qgfn/src/gflownet/data/mix_iterator.py code served (permissive licence) · get_code("d8821f0b8834c2ef") |
| sqlite_load | Ran | yunglau/QGFN/utils/loaders.py code served (permissive licence) · get_code("6e3e278b5881d761") |
| try_to_load_df | Ran | yunglau/QGFN/utils/plotting.py code served (permissive licence) · get_code("70d87c0cd02ff899") |
| rna_sqlite_load | Not yet run | yunglau/QGFN/utils/loaders.py code served (permissive licence) · get_code("30dd48f7c0ea8071") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate, Q, to create greedier sampling policies which can be controlled by a mixing parameter. We show that several variants of the proposed method, QGFN, are able to improve on the number of high-reward samples generated in a variety of tasks without sacrificing diversity.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.05234")
get_code_for_paper("2402.05234")
have("2402.05234")
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