update

src/mcts copy.jl

@@ -12,6 +12,10 @@ using GeneralUtils
 
 # ---------------------------------------------- 100 --------------------------------------------- #
 
+"""
+  TODO\n
+  [] update docstring
+"""
 struct MCTSNode{T}
   state::T
   visits::Int
@@ -19,6 +23,10 @@ struct MCTSNode{T}
   children::Dict{T, MCTSNode}
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function select(node::MCTSNode, c::Float64)
     max_uct = -Inf
     selected_node = nothing
@@ -35,6 +43,10 @@ function select(node::MCTSNode, c::Float64)
     return selected_node
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function expand(node::MCTSNode, state::T, actions::Vector{T})
     for action in actions
         new_state = transition(node.state, action)  # Implement your transition function
@@ -44,6 +56,10 @@ function expand(node::MCTSNode, state::T, actions::Vector{T})
     end
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function simulate(state::T, max_depth::Int)
     total_reward = 0.0
     for _ in 1:max_depth
@@ -54,6 +70,10 @@ function simulate(state::T, max_depth::Int)
     return total_reward
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function backpropagate(node::MCTSNode, reward::Float64)
     node.visits += 1
     node.total_reward += reward
@@ -63,16 +83,29 @@ function backpropagate(node::MCTSNode, reward::Float64)
     end
 end
 
+"""
+  TODO\n
+  [] update docstring
+  [] implement transition()
+"""
+function transition(state, action)
+
+end
+
 # ------------------------------------------------------------------------------------------------ #
 #                    Create a complete example using the defined MCTS functions                    #
 # ------------------------------------------------------------------------------------------------ #
-function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, c::Float64)
+"""
+  TODO\n
+  [] update docstring
+"""
+function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, w::Float64)
     root = MCTSNode(initial_state, 0, 0.0, Dict())
 
     for _ in 1:max_iterations
         node = root
         while !is_leaf(node)
-            node = select(node, c)
+            node = select(node, w)
         end
 
         expand(node, node.state, actions)
@@ -94,12 +127,6 @@ actions = [-1, 0, 1]
 best_action = run_mcts(initial_state, actions, 1000, 10, 1.0)
 println("Best action to take: ", best_action)
 
-In this example, you define the MCTS algorithm with the UCT selection function and then create a complete example of using the MCTS algorithm to find the best action to take in a given state space with a set of actions. You can customize the transition function, action selection function, and parameters to suit your specific problem domain.
-
-
-
-
-
 
 
 

src/mcts.jl

@@ -12,6 +12,10 @@ using GeneralUtils
 
 # ---------------------------------------------- 100 --------------------------------------------- #
 
+"""
+  TODO\n
+  [] update docstring
+"""
 struct MCTSNode{T}
   state::T
   visits::Int
@@ -19,6 +23,10 @@ struct MCTSNode{T}
   children::Dict{T, MCTSNode}
 end
 
+""" Traversing tree
+  TODO\n
+  [] update docstring
+"""
 function select(node::MCTSNode, c::Float64)
     max_uct = -Inf
     selected_node = nothing
@@ -35,6 +43,10 @@ function select(node::MCTSNode, c::Float64)
     return selected_node
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function expand(node::MCTSNode, state::T, actions::Vector{T})
     for action in actions
         new_state = transition(node.state, action)  # Implement your transition function
@@ -44,6 +56,10 @@ function expand(node::MCTSNode, state::T, actions::Vector{T})
     end
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function simulate(state::T, max_depth::Int)
     total_reward = 0.0
     for _ in 1:max_depth
@@ -54,6 +70,10 @@ function simulate(state::T, max_depth::Int)
     return total_reward
 end
 
+"""
+  TODO\n
+  [] update docstring
+"""
 function backpropagate(node::MCTSNode, reward::Float64)
     node.visits += 1
     node.total_reward += reward
@@ -63,16 +83,38 @@ function backpropagate(node::MCTSNode, reward::Float64)
     end
 end
 
+"""
+  TODO\n
+  [] update docstring
+  [] implement transition()
+"""
+function transition(state, action)
+
+end
+
+"""
+  TODO\n
+  [] update docstring
+  [] implement isLeaf()
+"""
+function isLeaf(node::MCTSNode)
+
+end
+
 # ------------------------------------------------------------------------------------------------ #
 #                    Create a complete example using the defined MCTS functions                    #
 # ------------------------------------------------------------------------------------------------ #
-function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, c::Float64)
+"""
+  TODO\n
+  [] update docstring
+"""
+function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, w::Float64)
     root = MCTSNode(initial_state, 0, 0.0, Dict())
 
     for _ in 1:max_iterations
         node = root
-        while !is_leaf(node)
+        while !isLeaf(node)
-            node = select(node, c)
+            node = select(node, w)
         end
 
         expand(node, node.state, actions)
@@ -94,12 +136,6 @@ actions = [-1, 0, 1]
 best_action = run_mcts(initial_state, actions, 1000, 10, 1.0)
 println("Best action to take: ", best_action)
 
-In this example, you define the MCTS algorithm with the UCT selection function and then create a complete example of using the MCTS algorithm to find the best action to take in a given state space with a set of actions. You can customize the transition function, action selection function, and parameters to suit your specific problem domain.
-
-
-
-
-