update

src/interface.jl

@@ -26,27 +26,23 @@ using ..type, ..mcts, ..util
   - `horizontalSampleSimulationPhase::Integer`
     a number of child state MCTS sample at each node during simulation's expansion phase (default: 3)
   - `maxSimulationDepth::Integer`
-
     a number of levels MCTS goes during simulation phase (default: 3)
   - `maxiterations::Integer`
-
     a number of iteration MCTS goes thru expansion -> simulation -> backpropagation cycle (default: 10)
   - `explorationweight::Number`
     exploration weight controls how much MCTS should explore new state instead of exploit 
     a known state. 1.0 balance between exploration and exploitation like 50%-50%. 2.0 makes MCTS
-
     aggressively explore new state (default: 1.0)
   - `earlystop::Union{Function,Nothing}`
     optional function to check early stopping condition (default: nothing)
   - `saveSimulatedNode::Bool`
     whether to save nodes created during simulation phase (default: false)
-
-
-
+  - `multithread::Bool`
+    whether to use multithreading during simulation (default: false)
 
 # Returns
-  - `NamedTuple{(:mctstree, :bestNextState, :bestFinalState), Tuple{MCTSNode, T, T}}`
-    - mctstree: the complete MCTS tree with root node
+  - `NamedTuple{(:root, :bestNextState, :bestFinalState), Tuple{MCTSNode, T, T}}`
+    - root: the complete MCTS tree with root node
     - bestNextState: the best immediate next state
     - bestFinalState: the best final state along the best trajectory
 
@@ -67,8 +63,8 @@ function runMCTS(
   explorationweight::Number=1.0,
   earlystop::Union{Function,Nothing}=nothing,
   saveSimulatedNode::Bool=false,
-  multithread=false) where {T<:Any}
-# )::NamedTuple{(:bestNextState, :bestFinalState),Tuple{T,T}} where {T<:Any}
+  multithread=false
+  )::NamedTuple{(:root, :bestNextState, :bestFinalState),Tuple{MCTSNode,T,T}} where {T<:Any}
 
   root = MCTSNode("root", initialstate, 0, 0, 0, 0, false, nothing, Dict{String,MCTSNode}(),
                   Dict{Symbol,Any}())
@@ -121,7 +117,29 @@ function runMCTS(
   return (root=root, bestNextState=bestNextState.state, bestFinalState=besttrajectory.state)
 end
 
+""" Search the best action to take for a given state and task 
 
+# Arguments
+- `node::MCTSNode`
+    current node to simulate from
+- `transition::Function`
+    a function that defines how the state transitions
+- `transitionargs::NamedTuple`
+    arguments for transition function
+
+# Keyword Arguments
+- `maxSimulationDepth::Integer`
+    a number of levels MCTS goes during simulation phase (default: 3)
+- `horizontalSampleSimulationPhase::Integer`
+    a number of child states MCTS samples at each node during simulation phase (default: 3)
+- `saveSimulatedNode::Bool`
+    whether to save nodes created during simulation phase (default: false)
+- `multithread::Bool`
+    whether to use multithreading during simulation (default: false)
+
+# Returns
+  Nothing, but updates the node's reward and visit count through backpropagation
+"""
 function simulateThenBackpropagate(node::MCTSNode, transition::Function, transitionargs::NamedTuple; 
                 maxSimulationDepth::Integer=3, horizontalSampleSimulationPhase::Integer=3,
                 saveSimulatedNode::Bool=false,
@@ -209,9 +227,6 @@ end
 
 
 
-
-
-