Comments (1)
If distribution is non-reparametrizable (e.g., Gamma):
- check that it random var does not depend on state or action fluents; if it does, raise ValueError
- recursively get sub-expressions shapes and broadcast them together
- pre-compile random var expression to tf.distribution with scope of non-fluents and interm-fluents that only depend on non-fluents
- append (dist, shape.as_list()) to noise list (e.g, exogenous event)
- wrap sample tensor as TensorFluent object when compiling re-parametrized random var
References:
pyrddl.expr.Expression.scope
pyrddl.rddl.RDDL.fluent_table
pyrddl.pvariable.PVariable.is_state_fluent()
andpyrddl.pvariable.PVariable.is_action_fluent()
from rddl2tf.
Related Issues (13)
- Create package for core modules fluent.py, fluentshape.py, and fluentscope.py
- Port scripts/rddl2tf to click CLI app
- Add Makefile for build, publish and testing automation
- Update README badges to include Python and pypi version
- Migration to TF2. HOT 2
- Error when running Programmatic mode example HOT 1
- Is there a way to use it with Pytorch?
- Define compiler interface with required methods and properties
- Refactor low-level rddl2tf auxiliary methods
- Implement log-likelihood-based compilation mode
- Implement determinization-based compilation mode
- Add --debug mode and log compilation information
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