update

Project.toml

@@ -1,7 +1,7 @@
 name = "LLMMCTS"
 uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.1.3"
+version = "0.1.4"
 
 [deps]
 GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"

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

@@ -9,7 +9,6 @@ using ..type, ..mcts, ..util
 # ---------------------------------------------- 100 --------------------------------------------- #
 
 
-
 """ Search the best action to take for a given state and task 
 
 # Arguments
@@ -34,7 +33,7 @@ using ..type, ..mcts, ..util
     a known state. 1.0 balance between exploration and exploitation like 50%-50%. 2.0 makes MCTS
     aggressively explore new state (default: 1.0)
   - `earlystop::Union{Function,Nothing}`
-    optional function to check early stopping condition (default: nothing)
+    optional function to check early stopping condition if it is satisfied, MCTS will break iterations (default: nothing)
   - `saveSimulatedNode::Bool`
     whether to save nodes created during simulation phase (default: false)
   - `multithread::Bool`
@@ -63,12 +62,16 @@ function runMCTS(
   explorationweight::Number=1.0,
   earlystop::Union{Function,Nothing}=nothing,
   saveSimulatedNode::Bool=false,
-  multithread=false
-  )::NamedTuple{(:root, :bestNextState, :bestFinalState),Tuple{MCTSNode,T,T}} where {T<:Any}
+  multithread=false,
+  )::NamedTuple{(:root, :bestNextState, :bestTerminalState, :highValueStateList),
+                Tuple{MCTSNode,T,T,Vector{Dict{Symbol,Any}}}} where {T<:Any}
 
   root = MCTSNode("root", initialstate, 0, 0, 0, 0, false, nothing, Dict{String,MCTSNode}(),
                   Dict{Symbol,Any}())
 
+  # storage for holding all high reward terminal nodes
+  highValueState = 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,
+                  highValueState=highValueState,
+                  )
         end
       else
         for (leafNodeKey, leafNode) in node.children
@@ -99,7 +108,8 @@ function runMCTS(
             maxSimulationDepth=maxSimulationDepth, 
             horizontalSampleSimulationPhase=horizontalSampleSimulationPhase,
             saveSimulatedNode=saveSimulatedNode,
-                  multithread=multithread)
+            multithread=multithread,
+            highValueState=highValueState)
         end
       end
     end
@@ -110,11 +120,24 @@ function runMCTS(
     end
   end
 
-  # select the best next state and the best final state
+  # select the best next state and the best terminal state along the best trajectory
   bestNextState = selectBestNextNode(root)
-  besttrajectory = selectBestTrajectoryNode(root)
+  bestTerminalState = selectBestTrajectoryNode(root)
 
-  return (root=root, bestNextState=bestNextState.state, bestFinalState=besttrajectory.state)
+  # take all high value state from highValueState channel and put it in a list
+  highValueStateList = Vector{Dict{Symbol, Any}}()
+  while !isempty(highValueState)
+    push!(highValueStateList, take!(highValueState))
+  end
+
+  result = (
+            root=root, 
+            bestNextState=bestNextState.state, 
+            bestTerminalState=bestTerminalState.state,
+            highValueStateList=highValueStateList
+            )
+
+  return result
 end
 
 """ Search the best action to take for a given state and task 
@@ -143,11 +166,21 @@ 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,
+                highValueState=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 highValueState
+  if highValueState !== nothing && 
+    terminalstate !== nothing &&
+    terminalstate[:reward] >= 8
+
+    put!(highValueState, deepcopy(terminalstate))
+  end
+
   backpropagate(node, simTrajectoryReward)
 
   # check if the user wants to keep the simulated node

src/mcts.jl

@@ -1,7 +1,7 @@
 module mcts
   
 export selectBestNextNode, selectBestTrajectoryNode, backpropagate, isleaf, isroot, selectChildNode,
-        expand, simulate, makeNewState
+        expand, simulate
 using Base.Threads
 using GeneralUtils
 
@@ -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
@@ -298,87 +298,88 @@ function simulate(node::MCTSNode, transition::Function, transitionargs::NamedTup
     end 
   end
 
-  return (simTrajectoryReward=simTrajectoryReward, terminalstate=terminalstate)
+  return (simTrajectoryReward=simTrajectoryReward, 
+          terminalstate=terminalstate)
 end
 
-""" Make new state
+# """ Make new state
 
-# Arguments
-  - `currentstate::T1`
-    Current state dictionary containing thought history and metadata
-  - `thoughtDict::T4`
-    Dictionary containing new thought and action
-  - `response::T2`
-    Response string from the environment
-  - `select::Union{T3, Nothing}`
-    Selection value or nothing
-  - `reward::T3`
-    Reward value for this state
-  - `isterminal::Bool`
-    Whether this state is terminal
+# # Arguments
+#   - `currentstate::T1`
+#     Current state dictionary containing thought history and metadata
+#   - `thoughtDict::T4`
+#     Dictionary containing new thought and action
+#   - `response::T2`
+#     Response string from the environment
+#   - `select::Union{T3, Nothing}`
+#     Selection value or nothing
+#   - `reward::T3`
+#     Reward value for this state
+#   - `isterminal::Bool`
+#     Whether this state is terminal
 
-# Return
-  - `Tuple{String, Dict{Symbol, <:Any}}`
-    A tuple containing:
-    - A unique node key string
-    - A new state dictionary with updated thought history and metadata
+# # Return
+#   - `Tuple{String, Dict{Symbol, <:Any}}`
+#     A tuple containing:
+#     - A unique node key string
+#     - A new state dictionary with updated thought history and metadata
 
-# Example
-```jldoctest
-julia> 
-```
+# # Example
+# ```jldoctest
+# julia> 
+# ```
 
-# Signature
-"""
-function makeNewState(currentstate::T1, thoughtDict::T4, response::T2, select::Union{T3, Nothing}, 
-  reward::T3, isterminal::Bool
-  )::Tuple{String, Dict{Symbol, <:Any}} where {T1<:AbstractDict, T2<:AbstractString, T3<:Number, T4<:AbstractDict}
+# # Signature
+# """
+# function makeNewState(currentstate::T1, thoughtDict::T4, response::T2, select::Union{T3, Nothing}, 
+#   reward::T3, isterminal::Bool
+#   )::Tuple{String, Dict{Symbol, <:Any}} where {T1<:AbstractDict, T2<:AbstractString, T3<:Number, T4<:AbstractDict}
 
-  # Find the latest thought key and index from current state's thought history
-  currentstate_latestThoughtKey, currentstate_latestThoughtIndice = 
-      GeneralUtils.findHighestIndexKey(currentstate[:thoughtHistory], "thought")
-  # Calculate next index for new thought/action
-  currentstate_nextIndice = 
-              currentstate_latestThoughtKey == :NA ? 1 : currentstate_latestThoughtIndice + 1
-  # Create new keys for thought and action based on next index
-  currentstate_latestThoughtKey = Symbol("thought_$currentstate_nextIndice")
-  latestActionKey = Symbol("action_$currentstate_nextIndice")
+#   # Find the latest thought key and index from current state's thought history
+#   currentstate_latestThoughtKey, currentstate_latestThoughtIndice = 
+#       GeneralUtils.findHighestIndexKey(currentstate[:thoughtHistory], "thought")
+#   # Calculate next index for new thought/action
+#   currentstate_nextIndice = 
+#               currentstate_latestThoughtKey == :NA ? 1 : currentstate_latestThoughtIndice + 1
+#   # Create new keys for thought and action based on next index
+#   currentstate_latestThoughtKey = Symbol("thought_$currentstate_nextIndice")
+#   latestActionKey = Symbol("action_$currentstate_nextIndice")
 
-  # Find the latest thought index from input thought dictionary
-  _, thoughtDict_latestThoughtIndice = 
-      GeneralUtils.findHighestIndexKey(thoughtDict, "thought")
+#   # Find the latest thought index from input thought dictionary
+#   _, thoughtDict_latestThoughtIndice = 
+#       GeneralUtils.findHighestIndexKey(thoughtDict, "thought")
   
-  # Determine thought and action keys from thought dictionary
-  thoughtDict_latestThoughtKey, thoughtDict_latestActionKey =
-  if thoughtDict_latestThoughtIndice == -1
-    (:thought, :action)
-  else
-    (
-      Symbol("thought_$thoughtDict_latestThoughtIndice"), 
-      Symbol("action_$thoughtDict_latestThoughtIndice"),
-    )
-  end
+#   # Determine thought and action keys from thought dictionary
+#   thoughtDict_latestThoughtKey, thoughtDict_latestActionKey =
+#   if thoughtDict_latestThoughtIndice == -1
+#     (:thought, :action)
+#   else
+#     (
+#       Symbol("thought_$thoughtDict_latestThoughtIndice"), 
+#       Symbol("action_$thoughtDict_latestThoughtIndice"),
+#     )
+#   end
 
-  # Create new state by deep copying current state
-  newstate = deepcopy(currentstate)
-  # Update thought history with new thought
-  newstate[:thoughtHistory][currentstate_latestThoughtKey] = 
-      thoughtDict[thoughtDict_latestThoughtKey]
-  # Update thought history with new action
-  newstate[:thoughtHistory][latestActionKey] = thoughtDict[thoughtDict_latestActionKey]
-  # Create and add new observation to thought history
-  newObservationKey = Symbol("observation_$(currentstate_nextIndice)")
-  newstate[:thoughtHistory][newObservationKey] = response
-  # Update state metadata
-  newstate[:reward] = reward
-  newstate[:select] = select
-  newstate[:isterminal] = isterminal
+#   # Create new state by deep copying current state
+#   newstate = deepcopy(currentstate)
+#   # Update thought history with new thought
+#   newstate[:thoughtHistory][currentstate_latestThoughtKey] = 
+#       thoughtDict[thoughtDict_latestThoughtKey]
+#   # Update thought history with new action
+#   newstate[:thoughtHistory][latestActionKey] = thoughtDict[thoughtDict_latestActionKey]
+#   # Create and add new observation to thought history
+#   newObservationKey = Symbol("observation_$(currentstate_nextIndice)")
+#   newstate[:thoughtHistory][newObservationKey] = response
+#   # Update state metadata
+#   newstate[:reward] = reward
+#   newstate[:select] = select
+#   newstate[:isterminal] = isterminal
 
-  # Generate unique ID for new node
-  newNodeKey = GeneralUtils.uuid4snakecase()
+#   # Generate unique ID for new node
+#   newNodeKey = GeneralUtils.uuid4snakecase()
 
-  return (newNodeKey, newstate)
-end
+#   return (newNodeKey, newstate)
+# end
 
 
 

test/Manifest.toml

@@ -0,0 +1,41 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.11.4"
+manifest_format = "2.0"
+project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
+
+[[deps.Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+version = "1.11.0"
+
+[[deps.InteractiveUtils]]
+deps = ["Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+version = "1.11.0"
+
+[[deps.Logging]]
+uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
+version = "1.11.0"
+
+[[deps.Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+version = "1.11.0"
+
+[[deps.Random]]
+deps = ["SHA"]
+uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+version = "1.11.0"
+
+[[deps.SHA]]
+uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+version = "0.7.0"
+
+[[deps.Serialization]]
+uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+version = "1.11.0"
+
+[[deps.Test]]
+deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
+uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+version = "1.11.0"

test/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"