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jerabaul29 avatar jerabaul29 commented on July 19, 2024

Hi,

You may have to dig a bit in the code to find where this impacts the code further, but this is basically set here if I remember well:

Cylinder2DFlowControlDRL/Cylinder2DFlowControlWithRL/ANN_controlled_flow_learning/env.py

Lines 101 to 102 in 44f526f

"min_value_jet_MFR": -1e-2,
"max_value_jet_MFR": 1e-2,

And the main position where this parameter is used is here:

Cylinder2DFlowControlDRL/Cylinder2DFlowControlWithRL/Env2DCylinder.py

Lines 889 to 903 in 44f526f

@property
def actions(self):
"""
Return the action space. Might include subdicts if multiple actions are available simultaneously.
Returns: dict of action properties (continuous, number of actions).
"""
# NOTE: we could also have several levels of dict in dict, for example:
# return { str(i): dict(continuous=True, min_value=0, max_value=1) for i in range(self.n + 1) }
return dict(type='float',
shape=(len(self.geometry_params["jet_positions"]), ),
min_value=self.optimization_params["min_value_jet_MFR"],
max_value=self.optimization_params["max_value_jet_MFR"])

(note that on newer TensorForce versions, the API may be slightly different).

The value is not exactly the same as in the paper, due to the different conventions used between the paper (non dimensional) and the code ("native" units, see the discussions in the "Confusing choices in the code" section of the Readme).

That should put you on the right track, closing, if anything is unclear feel free to re-open.

from cylinder2dflowcontroldrl.

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