update

src/mcts.jl

@@ -196,51 +196,22 @@ end
 # Return
   - None
 
-# TODO
-  - [WORKING] implement multithreads
-
 # Signature
 """
-function expand(node::MCTSNode, transition::Function, transitionargs::NamedTuple; 
-          totalsample::Integer=3)
-  
-  # not use Any[] because I want to preserve result order
-  results = Vector{Any}(undef, totalsample) 
-
-  @sync for i in 1:totalsample
-      @spawn begin
-        result = transition(deepcopy(node.state), deepcopy(transitionargs))
-        results[i] = result
-      end
-  end
-  
-  for result in results
-    newNodeKey::AbstractString = result[:newNodeKey]
-    newstate::AbstractDict = result[:newstate]
-    progressvalue::Integer = result[:progressvalue]
-
-    """
-    [] newNodeKey ∉ keys(node.children). 
-          New state may have semantic vector close enought to 
-        one of existing child state. Which can be assume that they are the same state 
-        semantically-wise i.e. De javu. This could be used to recall lessons for this 
-        similar situation to improve decisionMaker and evaluator. 
-    """
-    if newNodeKey ∉ keys(node.children)
-      node.children[newNodeKey] = 
-        MCTSNode(newNodeKey, newstate, 0, progressvalue, 0, newstate[:reward], 
-          newstate[:isterminal], node, Dict{String, MCTSNode}())
-    end
-  end
-end
 # function expand(node::MCTSNode, transition::Function, transitionargs::NamedTuple; 
 #           totalsample::Integer=3)
   
-#   nthSample = 0
-#   while true
-#     nthSample += 1
-#     if nthSample <= totalsample
-#       result = transition(node.state, transitionargs)
+#   # not use Any[] because I want to preserve result order
+#   results = Vector{Any}(undef, totalsample) 
+
+#   @sync for i in 1:totalsample
+#       @spawn begin
+#         result = transition(deepcopy(node.state), deepcopy(transitionargs))
+#         results[i] = result
+#       end
+#   end
+  
+#   for result in results
 #     newNodeKey::AbstractString = result[:newNodeKey]
 #     newstate::AbstractDict = result[:newstate]
 #     progressvalue::Integer = result[:progressvalue]
@@ -257,11 +228,37 @@ end
 #         MCTSNode(newNodeKey, newstate, 0, progressvalue, 0, newstate[:reward], 
 #           newstate[:isterminal], node, Dict{String, MCTSNode}())
 #     end
-#     else
-#       break
-#     end
 #   end
 # end
+function expand(node::MCTSNode,transition::Function, transitionargs::NamedTuple; 
+          totalsample::Integer=3)
+
+  nthSample = 0
+  while true
+    nthSample += 1
+    if nthSample <= totalsample
+      result = transition(node.state, transitionargs)
+      newNodeKey::AbstractString = result[:newNodeKey]
+      newstate::AbstractDict = result[:newstate]
+      progressvalue::Integer = result[:progressvalue]
+
+      """
+      [] newNodeKey ∉ keys(node.children). 
+            New state may have semantic vector close enought to 
+          one of existing child state. Which can be assume that they are the same state 
+          semantically-wise i.e. De javu. This could be used to recall lessons for this 
+          similar situation to improve decisionMaker and evaluator. 
+      """
+      if newNodeKey ∉ keys(node.children)
+        node.children[newNodeKey] = 
+          MCTSNode(newNodeKey, newstate, 0, progressvalue, 0, newstate[:reward], 
+            newstate[:isterminal], node, Dict{String, MCTSNode}())
+      end
+    else
+      break
+    end
+  end
+end
 
 
 """ Simulate interactions between agent and environment