Interface changed to make state simpler

DecMCTS.py

 import networkx as nx
+from copy import deepcopy
 from math import log
 import numpy as np
 
@@ -77,7 +78,14 @@ class Tree:
             reward/available actions in coordination with others
     """
 
-    def __init__(self, data, reward_func, avail_actions_func, comm_n, c_p=1, time_func=None):
+    def __init__(self, 
+            data, 
+            reward_func, 
+            avail_actions_func, 
+            comm_n, 
+            robot_id, 
+            c_p=1, 
+            time_func=None):
 
         self.data = data
         self.graph = nx.DiGraph()
@@ -85,6 +93,7 @@ class Tree:
         self.available_actions = avail_actions_func
         self.time_func = time_func
         self.c_p = c_p
+        self.id = robot_id
         self.comms = {} # Plan with no robots initially
         self.comm_n = comm_n # number of action dists to communicate
 
@@ -160,13 +169,16 @@ class Tree:
         """
 
         node_path = self.graph.node[node_id]["action_seq"]
-        other_paths = {k:self.comms[k].random_action() for k in self.comms}
+        all_paths = {k:self.comms[k].random_action() for k in self.comms}
+        all_paths[self.id] = node_path
 
-        return (node_path, other_paths)
+        return all_paths
 
 
     def _null_state(self, state):
-        return ([], state[1])
+        temp = deepcopy(state)
+        temp[self.id] = []
+        return temp
 
     
     def _expansion(self, start_node):

README.md

@@ -30,7 +30,7 @@ def avail_actions(data, state):
 # State is a dictionary with keys being robot IDs, and values
 # are a list of actions taken from the starting position
 def reward(dat, state):
-    each_robot_sum = [sum(state[1][a]) for a in state[1]]
+    each_robot_sum = [sum(state[a]) for a in state]
     return sum(each_robot_sum)
 
 # Number of Action Sequences to communicate

test/test.py

@@ -7,17 +7,15 @@ def avail_actions(data, state):
     return [1,2,3,4,5]
 
 def reward(dat, state):
-    other_robots = [sum(state[1][a]) for a in state[1]]
-    #if sum(other_robots) + sum(state[0]) >25:
-    #    return 0
-    return sum(state[0]) + sum(other_robots)
+    each_robot_sum= [sum(state[a]) for a in state]
+    return sum(each_robot_sum)
 
 comm_n = 5
 
 
-tree1 = Tree(data, reward, avail_actions, comm_n)
+tree1 = Tree(data, reward, avail_actions, comm_n, 1)
 
-tree2 = Tree(data, reward, avail_actions, comm_n)
+tree2 = Tree(data, reward, avail_actions, comm_n, 2)
 
 for i in range(350):
     tree1.grow()