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src/interface.jl

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+module interface
+
+export runMCTS
+
+using ..type, ..mcts
+
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+
+
+""" Search the best action to take for a given state and task 
+
+# Arguments
+  - `a::agent`
+    one of Yiem's agents
+  - `initial state`
+    initial state
+  - `decisionMaker::Function`
+    decide what action to take
+  - `evaluator::Function`
+    assess the value of the state
+  - `reflector::Function`
+    generate lesson from trajectory and reward
+  - `isterminal::Function`
+    determine whether a given state is a terminal state
+  - `n::Integer`
+    how many times action will be sampled from decisionMaker 
+  - `w::Float64`
+    exploration weight. Value is usually between 1 to 2. 
+    Value 1.0 makes MCTS balance between exploration and exploitation like 50%-50%
+    Value 2.0 makes MCTS aggressively search the tree
+  
+# Return
+  - `plan::Vector{Dict}`
+    best plan
+
+# Example
+```jldoctest
+julia> 
+```
+
+# TODO
+  [] update docstring
+  [] return best action
+
+# Signature
+"""
+function runMCTS(
+  workDict::Dict{Symbol, Any}, 
+  initialState,
+  decisionMaker::Function, 
+  evaluator::Function, 
+  reflector::Function,
+  transition::Function,
+  ;
+  totalsample::Integer=3, 
+  maxDepth::Integer=3, 
+  maxiterations::Integer=10, 
+  explorationweight::Number=1.0,
+  )
+  
+  root = MCTSNode("root", initialState, 0, 0, 0, 0, false, nothing, Dict{String, MCTSNode}())
+
+  for nth in 1:maxiterations
+    node = root
+    node.visits += 1
+
+    while !isleaf(node)
+      node = UCTselect(node, explorationweight)
+    end
+    if node.isterminal
+      # MCTS arrive at the leaf node that is also a terminal state,
+      # do nothing then go directly to backpropagation
+      backpropagate(leafNode, node.reward)
+    else
+      expand(workDict, node, decisionMaker, evaluator, reflector; totalsample=totalsample)
+      leafNode = selectChildNode(node)
+      simTrajectoryReward, terminalstate = simulate(workDict, leafNode, decisionMaker, evaluator, 
+                                    reflector; maxDepth=maxDepth, totalsample=totalsample)
+      if terminalstate !== nothing  #XXX not sure why I need this 
+        terminalstate[:totalTrajectoryReward] = simTrajectoryReward
+      end
+
+      #[] write best state to file if it has higher simTrajectoryReward. Use to improve evaluation
+      # open("trajectory.json", "w") do io
+      #   JSON3.pretty(io, terminalstate)
+      # end
+
+      backpropagate(leafNode, simTrajectoryReward)
+    end
+  end
+
+  bestNextState = selectBestNextState(root)
+  besttrajectory = selectBestTrajectory(root)
+
+  return (bestNextState.state, besttrajectory.state)
+end
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+end # module interface