fix python3 nodes bug, and sim_state bug

pydecmcts/DecMCTS.py

@@ -5,6 +5,7 @@ from copy import copy
 from math import log
 import numpy as np
 
+
 def _UCT(mu_j, c_p, n_p, n_j):
     if n_j ==0:
         return float("Inf")
@@ -117,8 +118,7 @@ class Tree:
                 )
 
         # Set Action sequence as nothing for now
-        self.my_act_dist = ActionDistribution([self.graph.node[1]["state"]],[1])
-
+        self.my_act_dist = ActionDistribution([self.graph.nodes[1]["state"]],[1])
 
         self._expansion(1)
 
@@ -127,7 +127,6 @@ class Tree:
         """
         wrapper for code readability
         """
-
         return list(self.graph.predecessors(node_id))[0]
 
 
@@ -137,7 +136,7 @@ class Tree:
         """
 
         # N for parent
-        n_p = self.graph.node[self._parent(children[0])]["N"]
+        n_p = self.graph.nodes[self._parent(children[0])]["N"]
 
         # UCT values for children
         uct = [_UCT(node["mu"], self.c_p, n_p, node["N"]) 
@@ -166,8 +165,8 @@ class Tree:
         temp.pop(1, None)
 
         top_n_nodes = sorted(temp, key=temp.get, reverse=True)[:self.comm_n]
-        X = [self.graph.node[n]["best_rollout"] for n in top_n_nodes if self.graph.node[n]["N"]>0]
-        q = [self.graph.node[n]["mu"]**2 for n in top_n_nodes if self.graph.node[n]["N"]>0]
+        X = [self.graph.nodes[n]["best_rollout"] for n in top_n_nodes if self.graph.nodes[n]["N"]>0]
+        q = [self.graph.nodes[n]["mu"]**2 for n in top_n_nodes if self.graph.nodes[n]["N"]>0]
         self.my_act_dist = ActionDistribution(X,q)
         return True
 
@@ -182,14 +181,14 @@ class Tree:
         """
 
         system_state = {k:self.comms[k].random_action() for k in self.comms}
-        system_state[self.id] = self.graph.node[node_id]["state"]
+        system_state[self.id] = self.graph.nodes[node_id]["state"]
 
         return system_state
 
 
     def _null_state(self, state):
         temp = copy(state)
-        temp[self.id] = self.graph.node[1]["state"] # Null state is if robot still at root node
+        temp[self.id] = self.graph.nodes[1]["state"] # Null state is if robot still at root node
         return temp
 
     
@@ -202,7 +201,7 @@ class Tree:
 
         options = self.available_actions(
             self.data,
-            self.graph.node[start_node]["state"],
+            self.graph.nodes[start_node]["state"],
             self.id
         )
 
@@ -216,7 +215,7 @@ class Tree:
                     mu = 0,
                     best_reward = 0,
                     N = 0,
-                    state=self.state_store(self.data, self.graph.node[start_node]["state"], o, self.id)
+                    state=self.state_store(self.data, self.graph.nodes[start_node]["state"], o, self.id)
                     )
 
             self.graph.add_edge(start_node, len(self.graph))
@@ -249,7 +248,7 @@ class Tree:
         best_reward = float("-Inf")
         best_rollout = None
         for i in range(nsims):
-            temp_state = self.graph.node[start_node]["state"]
+            temp_state = self.graph.nodes[start_node]["state"]
             state[self.id] = temp_state
 
             d = 0 # depth
@@ -259,7 +258,7 @@ class Tree:
                 # Get the available actions
                 options = self.sim_available_actions(
                             self.data,
-                            state,
+                            state[self.id],
                             self.id
                         )
 
@@ -285,27 +284,27 @@ class Tree:
 
         avg_reward = avg_reward / nsims
 
-        self.graph.node[start_node]["mu"] = avg_reward
-        self.graph.node[start_node]["best_reward"] = best_reward
-        self.graph.node[start_node]["N"] = 1
-        self.graph.node[start_node]["best_rollout"] = copy(best_rollout)
+        self.graph.nodes[start_node]["mu"] = avg_reward
+        self.graph.nodes[start_node]["best_reward"] = best_reward
+        self.graph.nodes[start_node]["N"] = 1
+        self.graph.nodes[start_node]["best_rollout"] = copy(best_rollout)
 
         ### BACKPROPOGATION
         while start_node!=1: #while not root node
 
             start_node = self._parent(start_node)
 
-            self.graph.node[start_node]["mu"] = \
-                    (gamma * self.graph.node[start_node]["mu"] * \
-                     self.graph.node[start_node]["N"] + avg_reward) \
-                    /(self.graph.node[start_node]["N"] + 1)
+            self.graph.nodes[start_node]["mu"] = \
+                    (gamma * self.graph.nodes[start_node]["mu"] * \
+                     self.graph.nodes[start_node]["N"] + avg_reward) \
+                    /(self.graph.nodes[start_node]["N"] + 1)
 
-            self.graph.node[start_node]["N"] = \
-                    gamma * self.graph.node[start_node]["N"] + 1
+            self.graph.nodes[start_node]["N"] = \
+                    gamma * self.graph.nodes[start_node]["N"] + 1
 
-            if best_reward > self.graph.node[start_node]["best_reward"]:
-                self.graph.node[start_node]["best_reward"] = best_reward
-                self.graph.node[start_node]["best_rollout"] = copy(best_rollout)
+            if best_reward > self.graph.nodes[start_node]["best_reward"]:
+                self.graph.nodes[start_node]["best_reward"] = best_reward
+                self.graph.nodes[start_node]["best_rollout"] = copy(best_rollout)
 
         self._update_distribution()
 

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='pydecmcts',
-    version='0.4',
+    version='0.5',
     packages=find_packages(include=['pydecmcts']),
     py_modules=['pydecmcts']
 )