Using Gabriel Crispino Code, implemented topological value iteration.
This repository contains a sample implementation that can solve MDPs represented by PDDLGym environments using the Value Iteration algorithm.
This implementation uses the function get_successor_states imported from PDDLGym's core module (pddlgym.core).
Since this feature is currently available in PDDLGym's repository but not in its latest pypi release as of now,
to use it you'll need to either clone the repository and install it locally or install it via pip by pointing to the repository.
You can do the former by setting up a virtual env (see here) or the latter by running the following:
$ pip install git+https://github.com/tomsilver/pddlgym
For usage instructions, run python main.py --help in the repository's root folder
The following command example can be used to solve the PDDLEnvBlocks-v0 environment, simulate an episode with the resulting policy and render each encountered observation:
$ python main.py --env PDDLEnvBlocks-v0 --simulate --render_and_save