update

src/LLMMCTS.jl

@@ -1,6 +1,6 @@
 module LLMMCTS
 
-  # export agent
+  export MCTSNode
 
 
   """ Order by dependencies of each file. The 1st included file must not depend on any other

src/interface.jl

@@ -69,6 +69,9 @@ function runMCTS(
   root = MCTSNode("root", initialstate, 0, 0, 0, 0, false, nothing, Dict{String,MCTSNode}(),
                   Dict{Symbol,Any}())
 
+  # [WORKING] storage for holding all high reward terminal nodes
+  highStateValueNode = Channel{Any}(100)
+
   for nth in 1:maxiterations
     node = root
     node.visits += 1
@@ -78,6 +81,10 @@ function runMCTS(
     end
 
     if node.isterminal
+      if node.state[:reward] >= 8
+        put!(highrewardNode, deepcopy(node.state))
+      end
+
       # MCTS arrive at the leaf node that is also a terminal state,
       # do nothing then go directly to backpropagation. It means the end of this iteration
       backpropagate(node, node.reward)
@@ -91,7 +98,9 @@ function runMCTS(
                   maxSimulationDepth=maxSimulationDepth, 
                   horizontalSampleSimulationPhase=horizontalSampleSimulationPhase,
                   saveSimulatedNode=saveSimulatedNode,
-                  multithread=multithread)
+                  multithread=multithread,
+                  highStateValueNode=highStateValueNode,
+                  )
         end
       else
         for (leafNodeKey, leafNode) in node.children
@@ -99,7 +108,8 @@ function runMCTS(
             maxSimulationDepth=maxSimulationDepth, 
             horizontalSampleSimulationPhase=horizontalSampleSimulationPhase,
             saveSimulatedNode=saveSimulatedNode,
-                  multithread=multithread)
+            multithread=multithread,
+            highStateValueNode=highStateValueNode)
         end
       end
     end
@@ -114,6 +124,8 @@ function runMCTS(
   bestNextState = selectBestNextNode(root)
   besttrajectory = selectBestTrajectoryNode(root)
 
+  #[WORKING] compare all high value answer then select the best one
+
   return (root=root, bestNextState=bestNextState.state, bestFinalState=besttrajectory.state)
 end
 
@@ -143,11 +155,21 @@ end
 function simulateThenBackpropagate(node::MCTSNode, transition::Function, transitionargs::NamedTuple; 
                 maxSimulationDepth::Integer=3, horizontalSampleSimulationPhase::Integer=3,
                 saveSimulatedNode::Bool=false,
-                multithread=false)
-  simTrajectoryReward, terminalstate = simulate(node, transition, transitionargs;
+                multithread=false,
+                highStateValueNode=Union{Nothing,Any}=nothing)
+  simTrajectoryReward, terminalstate = 
+    simulate(node, transition, transitionargs;
             maxSimulationDepth=maxSimulationDepth, 
             horizontalSample=horizontalSampleSimulationPhase,
             multithread=multithread)
+  # if a node has state value >= 8, store it in highStateValueNode
+  if highStateValueNode !== nothing && 
+    terminalstate !== nothing &&
+    terminalstate[:reward] >= 8
+
+    put!(highStateValueNode, deepcopy(terminalstate))
+  end
+
   backpropagate(node, simTrajectoryReward)
 
   # check if the user wants to keep the simulated node

src/mcts.jl

@@ -280,7 +280,7 @@ end
 """
 function simulate(node::MCTSNode, transition::Function, transitionargs::NamedTuple; 
   maxSimulationDepth::Integer=3, horizontalSample::Integer=3, multithread=false
-)::NamedTuple{(:simTrajectoryReward, :terminalstate), Tuple{<:Number, Union{Dict{Symbol, Any}, Nothing}}}
+  )::NamedTuple{(:simTrajectoryReward, :terminalstate), Tuple{<:Number, Union{Dict{Symbol, Any}, Nothing}}}
 
   simTrajectoryReward = 0.0
   terminalstate = nothing
@@ -298,7 +298,8 @@ function simulate(node::MCTSNode, transition::Function, transitionargs::NamedTup
     end 
   end
 
-  return (simTrajectoryReward=simTrajectoryReward, terminalstate=terminalstate)
+  return (simTrajectoryReward=simTrajectoryReward, 
+          terminalstate=terminalstate)
 end
 
 """ Make new state