update

src/mcts.jl

@@ -42,12 +42,16 @@ julia> state = Dict(
         )
 ```
 
+# TODO
+  [] update docstring
+
 # Signature
 """
 struct MCTSNode{T<:AbstractDict}
+  statekey::String
   state::T
   visits::Integer
-  stateValue::AbstractFloat
+  progressValue::Number
   children::Dict{String, MCTSNode}
 end
 
@@ -90,12 +94,16 @@ end
 """ Expand selected node 
 
 # Arguments
+  - `a::T1`
+    One of YiemAgent's agent
   - `node::MCTSNode`
     MCTS node
-  - `state::T`
+  - `state::T2`
     a state of a game. Can be a Dict or something else.
   - `decisionMaker::Function`
-    
+    a function that output Thought and Action
+  - `progressValueEstimator::Function`
+    a function that output trajectory progress score
   
 # Return
 
@@ -104,14 +112,10 @@ end
 julia> 
 ```
 
-# TODO
-  - [] update docstring
-  - [WORKING] implement the function
-
 # Signature
 """
 function expand(a::T1, node::MCTSNode, state::T2, decisionMaker::Function, 
-                stateValueEstimator::Function; n::Integer=3) where {T1<:agent, T2<:AbstractDict}
+                progressValueEstimator::Function; n::Integer=3) where {T1<:agent, T2<:AbstractDict}
 
   # sampling action from decisionMaker
   for sample in 1:n
@@ -120,15 +124,12 @@ function expand(a::T1, node::MCTSNode, state::T2, decisionMaker::Function,
     @show thoughtDict
     newStatekey, newstate = MCTStransition(a, node.state, thoughtDict)  #[] Implement your transition function
     
-    if newStatekey ∉ keys(node.children)# BUG should be "key of the newstate" here not newstate itself
-      node.children[newStatekey] = MCTSNode(newstate, 0, 0.0, Dict{String, MCTSNode}())
+    # add progressValueEstimator
+    _, progressValue = progressValueEstimator(a, newstate)
+
+    if newStatekey ∉ keys(node.children)
+      node.children[newStatekey] = MCTSNode(newStatekey, newstate, 0, progressValue, Dict{String, MCTSNode}())
     end
-
-    # add stateValueEstimator
-
-
-
-
   end 
 end
 
@@ -145,23 +146,24 @@ julia>
 
 # TODO
   - [] update docstring
-  - [] implement the function
+  - [WORKING] implement the function
   - [] reward only comes at terminal state
 
 # Signature
 """
 function simulate(state::T, max_depth::Int) where {T<:AbstractDict}
-    total_reward = 0.0
-    for _ in 1:max_depth
-      #[] Implement your action selection function based on highest stateValue
-      action = select_action(state) # current state 
-      state, reward = transition(state, action)  # Implement transition function to a new state
+  error("--> simulate")
+  total_reward = 0.0
+  for _ in 1:max_depth
+    #[] Implement your action selection function based on highest stateValue
+    action = select_action(state) # current state 
+    state, reward = transition(state, action)  # Implement transition function to a new state
 
-      #[] check for the terminal state
+    #[] check for the terminal state
 
-      total_reward += reward
-    end
-    return total_reward
+    total_reward += reward
+  end
+  return total_reward
 end
 
 """
@@ -332,7 +334,7 @@ end
     initial state
   - `decisionMaker::Function`
     decide what action to take
-  - `stateValueEstimator::Function`
+  - `progressValueEstimator::Function`
     assess the value of the state
   - `reflector::Function`
     generate lesson from trajectory and reward
@@ -361,7 +363,7 @@ function runMCTS(
   a::T1, 
   initialState, 
   decisionMaker::Function, 
-  stateValueEstimator::Function, 
+  progressValueEstimator::Function, 
   reflector::Function,
   isterminal::Function,
   n::Integer, 
@@ -369,7 +371,7 @@ function runMCTS(
   maxIterations::Integer, 
   w::Float64) where {T1<:agent}
   
-  root = MCTSNode(initialState, 0, 0.0, Dict{String, MCTSNode}())
+  root = MCTSNode("root", initialState, 0, 0.0, Dict{String, MCTSNode}())
   
   for _ in 1:maxIterations
     node = root
@@ -377,7 +379,7 @@ function runMCTS(
       node = select(node, w)
     end
 
-    expand(a, node, node.state, decisionMaker, stateValueEstimator, n=n)
+    expand(a, node, node.state, decisionMaker, progressValueEstimator, n=n)
     
     # from paper, just start simulation at this node. Not the node that newly expanded
     leaf_node = node