Branin and Hartmann proxies #297
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…proxy must now be negative so as to then have positive rewards; base proxy implements a base map_to_standard_range which can be used to remap proxy values back to an original range
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Yes that works, will be part of the base install.
Joseph (Mobile)
…On Wed, Mar 13, 2024 at 11:56 Alex ***@***.***> wrote:
***@***.**** commented on this pull request.
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On pyproject.toml
<#297 (comment)>
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@josephdviviano <https://github.com/josephdviviano> this PR adds two new
proxy classes that make use of a Python library that wasn't yet a
dependency, botorch <https://botorch.org/>. Is this the correct way of
adding it as a dependency? Anything else needs to be done? Thanks!
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alexhernandezgarcia
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This PR implements the Branin and Hartmann proxies, functions widely used in the optimisation and (multi-fidelity) active learning literature, including the Multi-Fidelity Active Learning with GFlowNets paper.
The Branin function is defined on the real plane and the Hartmann function on a 6D real hyper-plane. Therefore, these functions (proxies) can be used to train both grid (discrete) and cube (continuous) environments.
They rely on the botorch implementation. Therefore
botorchhas been added as a new dependency.Both proxies can be turned into multi-fidelity oracles via the parameter
fidelity.This PR includes a bit of additional clean-up and refactoring:
funcfrom environment config files.higher_is_betterfrom proxy config files.proxy_factorin the base environment, since it is assumed that proxy outputs will be negative and lower is better. If not, this can still be controlled with the beta parameter. In any case, this aspect should be revised in the future./gflownet/proxy/boxand similarly for the configuration files.