thiagopbueno / rddl2tf Goto Github PK

Define all required compilation methods and properties in a base class. Allow method overriding and reuse via **kwargs.

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!

Extend base Compiler class to handle determinization schemes when compiling random variables. Refactor utility functions.

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 re-parameterized distributions. Refactor utility functions.

Extend base Compiler class to handle log-likelihood of distributions. Refactor utility functions.

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?

Define object-oriented low-level rddl2tf API.