merge main

Author: guilyx

README.md

@@ -12,7 +12,7 @@
 
 A Python wrapper using JuliaPy for the PDDL.jl parser package and implementing its own planners.
 
-# Features
+## Features
 
 - Easy to use API for exploring new states
 - Depth First Search
@@ -23,7 +23,11 @@ A Python wrapper using JuliaPy for the PDDL.jl parser package and implementing i
     - Delete Relaxation Heuristics (Hmax, Hadd)
     - Critical Path Heuristic
 
-# Dependencies
+## Docker
+
+You can also use the project in a docker container using [docker-pythonpddl](https://github.com/APLA-Toolbox/docker-pythonpddl)
+
+## Dependencies
 
 - Install Python (3.7.5 is the tested version)
 
@@ -68,7 +72,7 @@ $ python ipc.py "path_to_domain.pddl" "path_to_problem.pddl" "path_to_desired_ou
 
 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.
 
-# Library Usage
+## Library Usage
 
 If using the jupyddl pip package:
 
@@ -85,7 +89,7 @@ $ git submodule update --init
 
 You should have a `pddl-examples` folder containing PDDL instances.
 
-## [AutomatedPlanner]
+### [AutomatedPlanner]
 
 ```python
 from jupyddl import AutomatedPlanner # takes some time because it has to instantiate the Julia interface
@@ -115,7 +119,7 @@ print(apl.get_actions_from_path(path))
 [<PyCall.jlwrap flip_row(r1)>, <PyCall.jlwrap flip_row(r3)>, <PyCall.jlwrap flip_column(c2)>]
 ```
 
-## [Data Analyst]
+### [Data Analyst]
 
 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 )
 
@@ -142,11 +146,11 @@ da.comparative_data_plot(collect_new_data=False) # uses data.json to plot the da
 da.comparative_astar_heuristic_plot() # compare results of astar with all available heuristics
 ```
 
-# Contribute
+## Contribute
 
 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).
 
-# Maintainers
+## Maintainers
 
 - Erwin Lejeune
 - Sampreet Sarkar

jupyddl/heuristics.py

@@ -127,6 +127,10 @@ def __facts_eq(self, facts_dict, facts_set):
                 return False
         return True
 
+<<<<<<< HEAD
+=======
+
+>>>>>>> b0a6f7114bd56e8d05402ea32199a4ae7bd498bd
 class CriticalPathHeuristic:
     def __init__(self, automated_planner, critical_path_level=1):
         class CPCache:
@@ -170,6 +174,7 @@ def compute(self, state):
                         if str(g) in fact_costs_str:
                             costs.append(fact_costs_str[str(g)])
                 if self.critical_path_level == 2:
+<<<<<<< HEAD
                     pairs_of_goals = [(g1, g2) for g1 in goals for g2 in goals if g1 != g2]
                     for gs in pairs_of_goals:
                         if str(gs[0]) in fact_costs_str and str(gs[1]) in fact_costs_str:
@@ -179,6 +184,38 @@ def compute(self, state):
                     for gs in triplets_of_goals:
                         if str(gs[0]) in fact_costs_str and str(gs[1]) in fact_costs_str and str(gs[2]) in fact_costs_str:
                             costs.append(fact_costs_str[str(gs[0])] + fact_costs_str[str(gs[1])] + fact_costs_str[str(gs[2])])
+=======
+                    pairs_of_goals = [
+                        (g1, g2) for g1 in goals for g2 in goals if g1 != g2
+                    ]
+                    for gs in pairs_of_goals:
+                        if (
+                            str(gs[0]) in fact_costs_str
+                            and str(gs[1]) in fact_costs_str
+                        ):
+                            costs.append(
+                                fact_costs_str[str(gs[0])] + fact_costs_str[str(gs[1])]
+                            )
+                if self.critical_path_level == 3:
+                    triplets_of_goals = [
+                        (g1, g2, g3)
+                        for g1 in goals
+                        for g2 in goals
+                        for g3 in goals
+                        if g1 != g2 and g1 != g3 and g2 != g3
+                    ]
+                    for gs in triplets_of_goals:
+                        if (
+                            str(gs[0]) in fact_costs_str
+                            and str(gs[1]) in fact_costs_str
+                            and str(gs[2]) in fact_costs_str
+                        ):
+                            costs.append(
+                                fact_costs_str[str(gs[0])]
+                                + fact_costs_str[str(gs[1])]
+                                + fact_costs_str[str(gs[2])]
+                            )
+>>>>>>> b0a6f7114bd56e8d05402ea32199a4ae7bd498bd
                 costs.insert(0, 0)
                 return max(costs)