update

src/interface.jl

@@ -47,7 +47,7 @@ julia>
 # Signature
 """
 function runMCTS(
-  workDict::Dict{Symbol, Any}, 
+  workDict::T1, 
   initialState,
   decisionMaker::Function, 
   evaluator::Function, 
@@ -58,7 +58,7 @@ function runMCTS(
   maxDepth::Integer=3, 
   maxiterations::Integer=10, 
   explorationweight::Number=1.0,
-  )
+  ) where {T1<:AbstractDict}
   
   root = MCTSNode("root", initialState, 0, 0, 0, 0, false, nothing, Dict{String, MCTSNode}())
 
@@ -74,7 +74,8 @@ function runMCTS(
       # do nothing then go directly to backpropagation
       backpropagate(leafNode, node.reward)
     else
-      expand(workDict, node, decisionMaker, evaluator, reflector; totalsample=totalsample)
+      expand(workDict, node, decisionMaker, evaluator, reflector, transition; 
+            totalsample=totalsample)
       leafNode = selectChildNode(node)
       simTrajectoryReward, terminalstate = simulate(workDict, leafNode, decisionMaker, evaluator, 
                                     reflector; maxDepth=maxDepth, totalsample=totalsample)

src/mcts.jl

@@ -1,7 +1,7 @@
 module mcts
   
 export selectBestNextState, selectBestTrajectory, backpropagate, isleaf, isroot, selectChildNode,
-        expand, mctstransition
+        expand, simulate, mctstransition
 
 using ..type
 
@@ -250,7 +250,7 @@ function expand(workDict::T1, node::MCTSNode, decisionMaker::Function, evaluator
   while true
     nthSample += 1
     if nthSample <= totalsample
-      thoughtDict = decisionMaker(a, node.state)
+      thoughtDict = decisionMaker(workDict, node.state)
       println("---> expand() sample $nthSample")
       pprintln(node.state[:thoughtHistory])
       pprintln(thoughtDict)
@@ -261,7 +261,7 @@ function expand(workDict::T1, node::MCTSNode, decisionMaker::Function, evaluator
       if newstate[:reward] < 0
         pprint(newstate[:thoughtHistory])
         newstate[:evaluation] = stateevaluation
-        newstate[:lesson] = reflector(a, newstate)
+        newstate[:lesson] = reflector(workDict, newstate)
 
         # store new lesson for later use
         lessonDict = copy(JSON3.read("lesson.json"))
@@ -288,6 +288,52 @@ function expand(workDict::T1, node::MCTSNode, decisionMaker::Function, evaluator
 end
 
 
+""" Simulate interactions between agent and environment
+
+# Arguments
+  - `a::T`
+    one of YiemAgent's agent
+  - `node::MCTSNode`
+    node that will be a simulation starting point.
+  - `decisionMaker::Function`
+    function that receive state return Thought and Action
+  
+# Return
+  - `simTrajectoryReward::Number`
+
+# Example
+```jldoctest
+julia> 
+```
+
+# TODO
+  - [] update docs
+
+# Signature
+"""
+function simulate(workDict::T, node::MCTSNode, decisionMaker::Function, evaluator::Function, 
+  reflector::Function; maxDepth::Integer=3, totalsample::Integer=3
+  )::Union{Tuple{Number, Dict{Symbol, <:Any}}, Tuple{Number, Nothing}} where {T<:AbstractDict}
+
+  simTrajectoryReward = 0.0
+  terminalstate = nothing
+  
+  for depth in 1:maxDepth
+    simTrajectoryReward += node.reward
+    if node.isterminal
+      terminalstate = node.state
+      break
+    else
+      expand(workDict, node, decisionMaker, evaluator, reflector; totalsample=totalsample)
+      node = selectChildNode(node)
+    end
+  end
+
+  return (simTrajectoryReward, terminalstate)
+end
+
+
+
 """ Get a new state
 
 # Arguments
@@ -329,7 +375,7 @@ julia>  thoughtDict = Dict(
 """
 function mctstransition(workDict::T1, transition::Function, state::T2, thoughtDict::T2
   )::Tuple{String, Dict{Symbol, <:Any}} where {T1<:AbstractDict, T2<:AbstractDict}
-
+  error("--> mctstransition")
   # actionname = thoughtDict[:action][:name]
   # actioninput = thoughtDict[:action][:input]