update

src/mcts.jl

@@ -1,6 +1,6 @@
 module mcts
   
-export selectBestNextState, selectBestTrajectory, backpropagate, isleaf, isroot, selectChildNode,
+export selectBestNextNode, selectBestTrajectoryNode, backpropagate, isleaf, isroot, selectChildNode,
         expand, simulate, makeNewState
 
 using GeneralUtils
@@ -20,18 +20,9 @@ using ..type
   - `childNode::MCTSNode`
     the highest value child node
 
-# Example
-```jldoctest
-julia> 
-```
-
-# TODO
-  - [] update docs
-  - [x] implement the function
-
 # Signature
 """
-function selectBestNextState(node::MCTSNode)::MCTSNode
+function selectBestNextNode(node::MCTSNode)::MCTSNode
   highestProgressValue = -1
   nodekey = nothing
 
@@ -72,20 +63,11 @@ end
   - `childNode::MCTSNode`
     the highest value child node
 
-# Example
-```jldoctest
-julia> 
-```
-
-# TODO
-  - [] update docs
-  - [x] implement the function
-
 # Signature
 """
-function selectBestTrajectory(node::MCTSNode)::MCTSNode
+function selectBestTrajectoryNode(node::MCTSNode)::MCTSNode
   while !isleaf(node)
-    node = selectBestNextState(node)
+    node = selectBestNextNode(node)
   end
 
   return node
@@ -99,14 +81,12 @@ end
     leaf node of a search tree
   - `simTrajectoryReward::T`
     total reward from trajectory simulation
+  - `discountRewardCoeff::AbstractFloat`
+    A discount reward coefficient to reduce future reward. The futher in the future the lower
+    reward it is now.
   
 # Return
-  - `No return`
-
-# Example
-```jldoctest
-julia> 
-```
+  - `None`
 
 # Signature
 """
@@ -166,11 +146,6 @@ isleaf(node::MCTSNode)::Bool = isempty(node.children)
   - `isrootnode::Bool`
     true if the given node is root node, false otherwise
 
-# Example
-```jldoctest
-julia> 
-```
-
 # Signature
 """
 isroot(node::MCTSNode)::Bool = node.nodekey == "root" ? true : false
@@ -187,11 +162,6 @@ isroot(node::MCTSNode)::Bool = node.nodekey == "root" ? true : false
   - `childNode::MCTSNode`
     the highest value child node
 
-# Example
-```jldoctest
-julia> 
-```
-
 # Signature
 """
 function selectChildNode(node::MCTSNode)::MCTSNode
@@ -201,9 +171,6 @@ function selectChildNode(node::MCTSNode)::MCTSNode
   # loop thought node children dictionary to find the highest progress value
   for (k, childNode) in node.children
     potential = childNode.progressvalue + childNode.reward
-    if childNode.reward > 0 #XXX for testing. remove when done.
-      println("")
-    end
     if potential > highestProgressValue
       highestProgressValue = potential
       nodekey = childNode.nodekey
@@ -214,34 +181,26 @@ function selectChildNode(node::MCTSNode)::MCTSNode
 end
 
 
-""" Expand selected node 
+""" Expand selected node.
 
 # Arguments
-  - `a::T1`
-    One of YiemAgent's agent
   - `node::MCTSNode`
     MCTS node
-  - `state::T2`
-    a state of a game. Can be a Dict or something else.
-  - `decisionMaker::Function`
-    a function that output Thought and Action
-  - `evaluator::Function`
-    a function that output trajectory progress score
+  - `transition::Function`
+    A function that handles state transition.
+  - `transitionargs::NamedTuple`
+    Arguments for transition()
+  - `totalsample::Integer`
+    Total number to sample from the current node (i.e. expand new node horizontally)
   
 # Return
+  - None
 
 # Example
 ```jldoctest
 julia> 
 ```
 
-# TODO
-  [] update docstring
-  [] try loop should limit to 3 times. if not succeed, skip
-  [] newNodeKey ∉ keys(node.children). New state may have semantic vector close enought to one of existing child state. Which can be assume that they are the same state semantically-wise. 
-  [x] store feedback -> state -> agent.
-
-
 # Signature
 """
 function expand(node::MCTSNode,transition::Function, transitionargs::NamedTuple; 
@@ -255,6 +214,14 @@ function expand(node::MCTSNode,transition::Function, transitionargs::NamedTuple;
       newNodeKey::AbstractString = result[:newNodeKey]
       newstate::AbstractDict = result[:newstate]
       progressvalue::Integer = result[:progressvalue]
+
+      """
+      [] newNodeKey ∉ keys(node.children). 
+            New state may have semantic vector close enought to 
+          one of existing child state. Which can be assume that they are the same state 
+          semantically-wise i.e. De javu. This could be used to recall lessons for this 
+          similar situation to improve decisionMaker and evaluator. 
+      """
       if newNodeKey ∉ keys(node.children)
         node.children[newNodeKey] = 
           MCTSNode(newNodeKey, newstate, 0, progressvalue, 0, newstate[:reward], 
@@ -270,30 +237,25 @@ 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
-  
+  - `transition::Function`
+    A user function that handles how state transition.
+  - `transitionargs::NamedTuple`
+    Arguments for everything the user will use within transition().
+  - `maxdepth::Integer`
+    maximum depth level MCTS goes vertically.
+  - totalsample::Integer
+    Total number to sample from the current node (i.e. expand new node horizontally)
+    
 # Return
-  - `simTrajectoryReward::Number`
-
-# Example
-```jldoctest
-julia> 
-```
-
-# TODO
-  - [] update docs
+  - `(simTrajectoryReward, terminalstate)::Union{Tuple{Number, Dict{Symbol, <:Any}}, Tuple{Number, Nothing}}`
 
 # Signature
 """
 function simulate(node::MCTSNode, transition::Function, transitionargs::NamedTuple; 
   maxdepth::Integer=3, totalsample::Integer=3
   )::Union{Tuple{Number, Dict{Symbol, <:Any}}, Tuple{Number, Nothing}}
-
   simTrajectoryReward = 0.0
   terminalstate = nothing