update

src/mcts.jl

@@ -124,28 +124,35 @@ julia>
 ```
 
 # TODO
-  - [] update docstring
+  [] update docstring
+  [] try loop should limit to 3 times. if not succeed, skip
 
 # Signature
 """
 function expand(a::T1, node::MCTSNode, decisionMaker::Function, 
                 progressValueEstimator::Function, isterminal::Function; n::Integer=3) where {T1<:agent}
 
-  # sampling action from decisionMaker
-  for sample in 1:n
-    thoughtDict = decisionMaker(a, node.state)
+  nthSample = 0
+  while nthSample < n
+    try
+      thoughtDict = decisionMaker(a, node.state)
 
-    newNodeKey, newstate, isterminalstate, reward = MCTStransition(a, node.state, thoughtDict, 
-                                                              isterminal)
-    
-    # add progressValueEstimator
-    stateevaluation, statevalue = progressValueEstimator(a, newstate)
+      newNodeKey, newstate, isterminalstate, reward = 
+                                      MCTStransition(a, node.state, thoughtDict, isterminal)
+      
+      # add progressValueEstimator
+      stateevaluation, statevalue = progressValueEstimator(a, newstate)
 
-    if newNodeKey ∉ keys(node.children)
-      node.children[newNodeKey] = MCTSNode(newNodeKey, newstate, 0, stateevaluation, statevalue, 
-                          reward, isterminalstate, node, Dict{String, MCTSNode}())
+      if newNodeKey ∉ keys(node.children)
+        node.children[newNodeKey] = MCTSNode(newNodeKey, newstate, 0, stateevaluation, statevalue, 
+                            reward, isterminalstate, node, Dict{String, MCTSNode}())
+      end
+      nthSample += 1
+    catch
+      # skip this child node if error occurs
+      println("retry node expand")
     end
-  end 
+  end
 end
 
 """
@@ -177,14 +184,9 @@ function simulate(a, node::MCTSNode, decisionMaker::Function, progressValueEstim
     if node.isterminal
       break
     else
-      try
-        simTrajectoryReward += node.reward
-        expand(a, node, decisionMaker, progressValueEstimator, isterminal, n=n)
-        node = selectChildNode(node)
-      catch
-        # if error occurs, break and try again later
-        break
-      end
+      simTrajectoryReward += node.reward
+      expand(a, node, decisionMaker, progressValueEstimator, isterminal, n=n)
+      node = selectChildNode(node)
     end
   end
 
@@ -354,7 +356,7 @@ function selectChildNode(node::MCTSNode)::MCTSNode
   # loop thought node children dictionary to find the highest progress value
   for (k, childNode) in node.children
     thisNodeProgressValue = childNode.statevalue + childNode.reward
-    if childNode.statevalue > highestProgressValue
+    if thisNodeProgressValue > highestProgressValue
       highestProgressValue = thisNodeProgressValue
       nodekey = childNode.nodekey
     end