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View Code? Open in Web Editor NEWRDDL2TensorFlow compiler.
Home Page: https://rddl2tf.readthedocs.io/
License: GNU General Public License v3.0
RDDL2TensorFlow compiler.
Home Page: https://rddl2tf.readthedocs.io/
License: GNU General Public License v3.0
After freshly installing rddl2tf
in a new conda environment, I am unable to run the Programmatic mode example in the README.
I get the following error
Traceback (most recent call last): File "testtf.py", line 8, in <module> compiler = Compiler(model) TypeError: Can't instantiate abstract class Compiler with abstract methods _compile_aggregation_expression, _compile_arithmetic_expression, _compile_boolean_expression, _compile_constant_expression, _compile_control_flow_expression, _compile_function_expression, _compile_pvariable_expression, _compile_random_variable_expression, _compile_relational_expression
Extend base Compiler class to handle determinization schemes when compiling random variables. Refactor utility functions.
Hi
I am interested in using RDDLGym in PyTorch. It seems this codebase is tightly coupled with TF 1. Where do you recommend I start in order to build a framework independent RDDLGym such that it can be used like any other RDDLGym environment and analogous to any DL library?
Thanks in advance!
Define object-oriented low-level rddl2tf API.
Extend base Compiler class to handle re-parameterized distributions. Refactor utility functions.
Define all required compilation methods and properties in a base class. Allow method overriding and reuse via **kwargs.
In my fork, I have successfully adjusted both rddl2tf and rddgym for TensorFlow 2, which is more easily available and maintained these days. Unfortunate is that we don't have a backward compatibility to TF1.
What would be the best offering we can make?
a) merge into the master, increasing a major/minor version.
b) proving a separate fork with a different name, say, rddl2tf_tf2 and rddlgym_tf2.
c) just leave it in a branch.
What do you say?
Extend base Compiler class to handle log-likelihood of distributions. Refactor utility functions.
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