update

src/mcts.jl

@@ -175,45 +175,6 @@ function expand(a::T1, node::MCTSNode, decisionMaker::Function,
     end
   end
 end
-# function expand(a::T1, node::MCTSNode, decisionMaker::Function, 
-#                 progressValueEstimator::Function, reflector::Function; n::Integer=3) where {T1<:agent}
-
-#   nthSample = 0
-#   while nthSample <= n
-#     nthSample += 1
-#     println("---> expand() sample $nthSample")
-#     thoughtDict = decisionMaker(a, node.state)
-
-#     newNodeKey, newstate, reward, isterminalstate = 
-#                                     MCTStransition(a, node.state, thoughtDict)
-    
-#     # add progressValueEstimator
-#     stateevaluation, statevalue = progressValueEstimator(a, newstate)
-
-#     if reward < 0
-#       pprint(newstate[:thoughtHistory])
-#       newstate[:evaluation] = stateevaluation
-#       newstate[:lesson] = reflector(a, newstate)
-
-#       # store new lesson for later use
-#       lessonDict = copy(JSON3.read("lesson.json"))
-#       latestLessonKey, latestLessonIndice = 
-#         GeneralUtils.findHighestIndexKey(state[:thoughtHistory], "lesson")
-#       nextIndice = latestLessonKey == :NA ? 1 : latestLessonIndice + 1
-#       newLessonKey = Symbol("lesson_$(nextIndice)")
-#       lessonDict[newLessonKey] = newstate
-#       open("lesson.json", "w") do io
-#         JSON3.pretty(io, lessonDict)
-#       end
-#       print("---> reflector()")
-#     end
-
-#     if newNodeKey ∉ keys(node.children)
-#       node.children[newNodeKey] = MCTSNode(newNodeKey, newstate, 0, statevalue, 
-#                           reward, isterminalstate, node, Dict{String, MCTSNode}())
-#     end
-#   end
-# end