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README.md

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A Python wrapper using JuliaPy for the PDDL.jl parser package and implementing its own planners.
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# Features
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## Features
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- Easy to use API for exploring new states
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- Depth First Search
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- A*
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- Goal Count Heuristic
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- Delete Relaxation Heuristics (Hmax, Hadd)
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## Docker
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# Dependencies
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You can also use the project in a docker container using [docker-pythonpddl](https://github.com/APLA-Toolbox/docker-pythonpddl)
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## Dependencies
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- Install Python (3.7.5 is the tested version)
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The output file will show the path with a list of state, the path with a list of action and the metrics proposed by IPC2018.
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# Library Usage
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## Library Usage
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If using the jupyddl pip package:
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You should have a `pddl-examples` folder containing PDDL instances.
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## [AutomatedPlanner]
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### [AutomatedPlanner]
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```python
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from jupyddl import AutomatedPlanner # takes some time because it has to instantiate the Julia interface
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[<PyCall.jlwrap flip_row(r1)>, <PyCall.jlwrap flip_row(r3)>, <PyCall.jlwrap flip_column(c2)>]
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```
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## [Data Analyst]
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### [Data Analyst]
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Make sure you have a pddl-examples folder where you run your environment that contains independent folders with "domain.pddl" and "problem.pddl" files, with those standard names. ( if you didn't generate with git submodule update )
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da.comparative_astar_heuristic_plot() # compare results of astar with all available heuristics
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```
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# Contribute
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## Contribute
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Open an issue to state clearly the contribution you want to make. Upon aproval send in a PR with the Issue referenced. (Implement Issue #No / Fix Issue #No).
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# Maintainers
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## Maintainers
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- Erwin Lejeune
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- Sampreet Sarkar

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