Merge pull request #16 from APLA-Toolbox/add-dijktra

Improve planners architecture

Author: guilyx

.github/workflows/pyapp.yml

@@ -17,13 +17,15 @@ jobs:
     - uses: julia-actions/setup-julia@v1
       with:
         version: '1.4.1'
-    - name: Install PDDL.jl
+    - name: Install Julia dependencies
       run: |
         julia --color=yes -e 'using Pkg; Pkg.add(Pkg.PackageSpec(path="https://github.com/APLA-Toolbox/PDDL.jl"))'
-    - name: Install dependencies
+        julia --color=yes -e 'using Pkg; Pkg.add(Pkg.PackageSpec(path="https://github.com/JuliaPy/PyCall.jl"))'
+    - name: Install Python dependencies
       run: |
         python -m pip install --upgrade pip
         python -m pip install julia
+        python -m pip install pycall
     - name: Lint with flake8
       run: |
         python -m pip install flake8
@@ -38,5 +40,4 @@ jobs:
     - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v1
       with:
-        file: ./coverage.xml
         name: codecov-umbrella

codecov.yml

@@ -0,0 +1,20 @@
+codecov:
+  require_ci_to_pass: yes
+
+coverage:
+  precision: 2
+  round: down
+  range: "70...100"
+
+parsers:
+  gcov:
+    branch_detection:
+      conditional: yes
+      loop: yes
+      method: no
+      macro: no
+
+comment:
+  layout: "reach,diff,flags,files,footer"
+  behavior: default
+  require_changes: no

main.py

@@ -1,4 +1,4 @@
-import src.pddl as plarser
+import src.automated_planner as plarser
 import argparse
 
 
@@ -11,9 +11,10 @@ def main():
     args_parser.add_argument("problem", type=str, help="PDDL problem file")
     args_parser.add_argument("-v", "--verbose", help="Increases the output's verbosity")
     args = args_parser.parse_args()
-    apla_tbx = plarser.AutomatedPlanning(args.domain, args.problem)
-    path = apla_tbx.get_actions_from_path(apla_tbx.breadth_first_search())
-    print(path)
+    apla_tbx = plarser.AutomatedPlanner(args.domain, args.problem)
+    path, time = apla_tbx.dijktra_best_first_search(time_it=True)
+    print(apla_tbx.get_actions_from_path(path))
+    print("Computation time: %.2f" % time)
 
 
 if __name__ == "__main__":

src/astar.py

@@ -0,0 +1,7 @@
+class AStarBestFirstSearch():
+    def __init__(self, automated_planner):
+        self.automated_planner = automated_planner
+    
+    def search(self):
+        print("-/!\- No path found -/!\-")
+        return []

src/automated_planner.py

@@ -0,0 +1,99 @@
+from .modules import loading_bar_handler
+from .bfs import BreadthFirstSearch
+from .dijkstra import DijkstraBestFirstSearch
+
+UI = False
+
+if UI:
+    loading_bar_handler(False)
+import julia
+
+_ = julia.Julia(compiled_modules=False)
+
+if UI:
+    loading_bar_handler(True)
+
+from julia import PDDL
+from time import time as now
+
+class AutomatedPlanner:
+    def __init__(self, domain_path, problem_path):
+        self.pddl = PDDL
+        self.domain = self.pddl.load_domain(domain_path)
+        self.problem = self.pddl.load_problem(problem_path)
+        self.initial_state = self.pddl.initialize(self.problem)
+
+    def __execute_action(self, action, state):
+        return self.pddl.execute(action, state)
+
+    def transition(self, state, action):
+        return self.pddl.transition(self.domain, state, action, check=False)
+        
+    def available_actions(self, state):
+        return self.pddl.available(state, self.domain)
+
+    def satisfies(self, asserted_state, state):
+        return self.pddl.satisfy(asserted_state, state, self.domain)[0]
+
+    def __retrace_path(self, node):
+        if not node:
+            return []
+        path = []
+        while node.parent:
+            path.append(node)
+            node = node.parent
+        path.reverse()
+        return path
+
+    def get_actions_from_path(self, path):
+        if not path:
+            print("Path is empty, can't operate...")
+            return []
+        actions = []
+        for node in path:
+            actions.append((node.parent_action, node.g_cost))
+
+        cost = self.pddl.get_value(path[-1].state, "total-cost")
+        if not cost:
+            return actions
+        else:
+            return (actions, cost)
+
+    def get_state_def_from_path(self, path):
+        if not path:
+            print("Path is empty, can't operate...")
+            return []
+        trimmed_path = []
+        for node in path:
+            trimmed_path.append(node.state)
+        return trimmed_path 
+
+    def breadth_first_search(self, time_it=False):
+        if time_it:
+            start_time = now()
+        bfs = BreadthFirstSearch(self)
+        last_node = bfs.search()
+        if time_it:
+            total_time = now() - start_time
+        path = self.__retrace_path(last_node)
+        if time_it:
+            return path, total_time
+        else:
+            return path, None
+
+    def dijktra_best_first_search(self, time_it=False):
+        if time_it:
+            start_time = now()
+        dijkstra = DijkstraBestFirstSearch(self)
+        last_node = dijkstra.search()
+        if time_it:
+            total_time = now() - start_time
+        path = self.__retrace_path(last_node)
+        if time_it:
+            return path, total_time
+        else:
+            return path, None
+
+
+if __name__ == "__main__":
+    ap = AutomatedPlanner("..data/domain.pddl", "..data/problem.pddl")

src/bfs.py

@@ -0,0 +1,26 @@
+from .node import Node
+
+class BreadthFirstSearch():
+    def __init__(self, automated_planner):
+        self.visited = []
+        self.automated_planner = automated_planner
+        self.init = Node(self.automated_planner.initial_state, automated_planner)
+        self.queue = [self.init]
+    
+    def search(self):
+        while self.queue:
+            current_node = self.queue.pop(0)
+            if current_node not in self.visited:
+                self.visited.append(current_node)
+
+                if self.automated_planner.satisfies(self.automated_planner.problem.goal, current_node.state):
+                    return current_node
+                
+                actions = self.automated_planner.available_actions(current_node.state)
+                for act in actions:
+                    child = Node(state=self.automated_planner.transition(current_node.state, act), automated_planner=self.automated_planner, parent_action=act, parent=current_node)
+                    if child in self.visited:
+                        continue
+                    self.queue.append(child)
+        print("!!! No path found !!!")
+        return None

src/dfs.py

@@ -0,0 +1,7 @@
+class DepthFirstSearch():
+    def __init__(self, automated_planner):
+        self.automated_planner = automated_planner
+    
+    def search(self):
+        print("-/!\- No path found -/!\-")
+        return []

src/dijkstra.py

@@ -0,0 +1,49 @@
+from .node import Node
+import math
+from .heuristics import zero_heuristic
+
+class DijkstraBestFirstSearch():
+    def __init__(self, automated_planner):
+        self.automated_planner = automated_planner
+        self.init = Node(self.automated_planner.initial_state, automated_planner, is_closed=False, is_open=True)
+        self.open_nodes_n = 1
+        self.nodes = dict()
+        self.nodes[self.__hash(self.init)] = self.init
+
+    def __hash(self, node):
+        sep = ", Dict{Symbol,Any}"
+        string = str(node.state)
+        return string.split(sep, 1)[0] + ")"
+
+    def search(self):
+        while self.open_nodes_n > 0:
+            current_key = min([n for n in self.nodes if self.nodes[n].is_open], key=(lambda  k: self.nodes[k].f_cost))
+            current_node = self.nodes[current_key]
+
+            if self.automated_planner.satisfies(self.automated_planner.problem.goal, current_node.state):
+                    return current_node
+
+            current_node.is_closed = True
+            current_node.is_open = False
+            self.open_nodes_n -= 1
+            
+            actions = self.automated_planner.available_actions(current_node.state)
+            for act in actions:
+                child = Node(state=self.automated_planner.transition(current_node.state, act), automated_planner=self.automated_planner, parent_action=act, parent=current_node, heuristic=zero_heuristic, is_closed=False, is_open=True)
+                child_hash = self.__hash(child)
+                if child_hash in self.nodes:
+                    if self.nodes[child_hash].is_closed:
+                        continue
+                    if not self.nodes[child_hash].is_open:
+                        self.nodes[child_hash] = child
+                        self.open_nodes_n += 1
+                    else:
+                        if child.g_cost < self.nodes[child_hash].g_cost:
+                            self.nodes[child_hash] = child
+                            self.open_nodes_n += 1
+
+                else:
+                    self.nodes[child_hash] = child
+                    self.open_nodes_n += 1
+        print("!!! No path found !!!")
+        return None

src/heuristics.py

@@ -0,0 +1,2 @@
+def zero_heuristic(state, pddl):
+    return 0

src/node.py

@@ -0,0 +1,27 @@
+class Node():
+    def __init__(self, state, automated_planner, is_closed=None, is_open=None, parent_action=None, parent=None, g_cost=0, heuristic=None):
+        self.state = state
+        self.parent_action = parent_action
+        self.parent = parent
+        temp_cost = automated_planner.pddl.get_value(state, "total-cost")
+        if temp_cost:
+            self.g_cost = temp_cost
+            if heuristic:
+                self.h_cost = heuristic(state, automated_planner)
+            else:
+                self.h_cost = 0
+            self.f_cost = self.g_cost + self.h_cost
+        else:
+            self.g_cost = 1
+            if heuristic:
+                self.h_cost = heuristic(state, automated_planner)
+            else:
+                self.h_cost = 0
+            self.f_cost = self.g_cost + self.h_cost
+
+    
+        self.is_closed = is_closed
+        self.is_open = is_open
+
+    def __lt__(self, other):
+        return self.f_cost <= other.f_cost