outline for simulation in decmcts

DecMCTS.py

@@ -23,7 +23,7 @@ class ActionDistribution:
     - q: probability of each action sequence (normalised in intialisation)
 
     """
-    
+
     def __init__(self, X, q):
         
         # Action sequence as provided
@@ -215,25 +215,36 @@ class Tree:
 
         ### SELECTION
         start_node = 1
+
+        # Sample actions of other robots
+        # NOTE: Sampling done at the begining for dependency graph reasons
+        state = self._get_system_state(start_node)
+
+        # Propagate down the tree
         while len(self._childNodes(start_node))>0:
             start_node = self._select(self._childNodes(start_node))
 
         ### EXPANSION
         self._expansion(start_node)        
-        
+
         ### SIMULATION
         avg_reward = 0
         best_reward = float("-Inf")
         for i in range(nsims):
-            # TODO
-            # SIMULATION NOT REQUIRED FOR BIGPRINT, HEURISTIC USED INSTEAD
+            # TODO TODO TODO
+            # Get the available actions
+            # "randomly" choose 1 - function provided by user
+            # add that to the actions of the current robot
+            # calculate the reward for that state
+            # avg reward += reward for sim/nsims
+            # if best reward so far, store the rollout in the new node
             pass
 
-        state = self._get_system_state(start_node)
+        state[self.id] = self.graph.node[start_node]["state"]
         avg_reward = self.reward(self.data, state) - self.reward(self.data, self._null_state(state))
         self.graph.node[start_node]["mu"] = avg_reward
         self.graph.node[start_node]["N"] = 1
-        
+
         ### BACKPROPOGATION
         while start_node!=1: #while not root node
 
@@ -245,9 +256,9 @@ class Tree:
 
             self.graph.node[start_node]["N"] = \
                     gamma * self.graph.node[start_node]["N"] + 1
-        
+
         self._update_distribution()
-        
+
         return avg_reward
 
 
@@ -260,7 +271,7 @@ class Tree:
         Save data which has been communicated to this tree
         Only receives from one robot at a time, call once 
         for each robot
-        
+
         Inputs:
         - comms_in
             - An Action distribution object