update

src/YiemAgent.jl

@@ -16,6 +16,9 @@ module YiemAgent
   include("llmfunction.jl")
   using .llmfunction
 
+  include("mcts.jl")
+  using .mcts
+
   include("interface.jl")
   using .interface
   

src/mcts copy.jl

@@ -0,0 +1,111 @@
+""" To implement a Monte Carlo Tree Search (MCTS) algorithm in Julia with the UCT (Upper Confidence 
+  Bound for Trees) selection function, you can follow the steps below: Define the necessary types 
+  and functions for the MCTS algorithm:
+"""
+
+module MCTS
+  
+# export 
+
+using Dates, UUIDs, DataStructures, JSON3, Random
+using GeneralUtils
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+struct MCTSNode{T}
+    state::T
+    visits::Int
+    total_reward::Float64
+    children::Dict{T, MCTSNode}
+end
+
+function select(node::MCTSNode, c::Float64)
+    max_uct = -Inf
+    selected_node = nothing
+
+    for (child_state, child_node) in node.children
+        uct_value = child_node.total_reward / child_node.visits + 
+                    c * sqrt(log(node.visits) / child_node.visits)
+        if uct_value > max_uct
+            max_uct = uct_value
+            selected_node = child_node
+        end
+    end
+
+    return selected_node
+end
+
+function expand(node::MCTSNode, state::T, actions::Vector{T})
+    for action in actions
+        new_state = transition(node.state, action)  # Implement your transition function
+        if new_state ∉ keys(node.children)
+            node.children[new_state] = MCTSNode(new_state, 0, 0.0, Dict{T, MCTSNode}())
+        end
+    end
+end
+
+function simulate(state::T, max_depth::Int)
+    total_reward = 0.0
+    for _ in 1:max_depth
+        action = select_action(state)  # Implement your action selection function
+        state, reward = transition(state, action)  # Implement your transition function
+        total_reward += reward
+    end
+    return total_reward
+end
+
+function backpropagate(node::MCTSNode, reward::Float64)
+    node.visits += 1
+    node.total_reward += reward
+    if !isempty(node.children)
+        best_child = argmax([child.total_reward / child.visits for child in values(node.children)])
+        backpropagate(node.children[best_child], -reward)
+    end
+end
+
+# ------------------------------------------------------------------------------------------------ #
+#                    Create a complete example using the defined MCTS functions                    #
+# ------------------------------------------------------------------------------------------------ #
+function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, c::Float64)
+    root = MCTSNode(initial_state, 0, 0.0, Dict())
+
+    for _ in 1:max_iterations
+        node = root
+        while !is_leaf(node)
+            node = select(node, c)
+        end
+
+        expand(node, node.state, actions)
+
+        leaf_node = node.children[node.state]
+        reward = simulate(leaf_node.state, max_depth)
+        backpropagate(leaf_node, reward)
+    end
+
+    best_child_state = argmax([child.total_reward / child.visits for child in values(root.children)])
+    return best_child_state
+end
+
+# Define your transition function and action selection function here
+
+# Example usage
+initial_state = 0
+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.
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+
+
+end

src/mcts.jl

@@ -0,0 +1,111 @@
+""" To implement a Monte Carlo Tree Search (MCTS) algorithm in Julia with the UCT (Upper Confidence 
+  Bound for Trees) selection function, you can follow the steps below: Define the necessary types 
+  and functions for the MCTS algorithm:
+"""
+
+module MCTS
+  
+# export 
+
+using Dates, UUIDs, DataStructures, JSON3, Random
+using GeneralUtils
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+struct MCTSNode{T}
+    state::T
+    visits::Int
+    total_reward::Float64
+    children::Dict{T, MCTSNode}
+end
+
+function select(node::MCTSNode, c::Float64)
+    max_uct = -Inf
+    selected_node = nothing
+
+    for (child_state, child_node) in node.children
+        uct_value = child_node.total_reward / child_node.visits + 
+                    c * sqrt(log(node.visits) / child_node.visits)
+        if uct_value > max_uct
+            max_uct = uct_value
+            selected_node = child_node
+        end
+    end
+
+    return selected_node
+end
+
+function expand(node::MCTSNode, state::T, actions::Vector{T})
+    for action in actions
+        new_state = transition(node.state, action)  # Implement your transition function
+        if new_state ∉ keys(node.children)
+            node.children[new_state] = MCTSNode(new_state, 0, 0.0, Dict{T, MCTSNode}())
+        end
+    end
+end
+
+function simulate(state::T, max_depth::Int)
+    total_reward = 0.0
+    for _ in 1:max_depth
+        action = select_action(state)  # Implement your action selection function
+        state, reward = transition(state, action)  # Implement your transition function
+        total_reward += reward
+    end
+    return total_reward
+end
+
+function backpropagate(node::MCTSNode, reward::Float64)
+    node.visits += 1
+    node.total_reward += reward
+    if !isempty(node.children)
+        best_child = argmax([child.total_reward / child.visits for child in values(node.children)])
+        backpropagate(node.children[best_child], -reward)
+    end
+end
+
+# ------------------------------------------------------------------------------------------------ #
+#                    Create a complete example using the defined MCTS functions                    #
+# ------------------------------------------------------------------------------------------------ #
+function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, c::Float64)
+    root = MCTSNode(initial_state, 0, 0.0, Dict())
+
+    for _ in 1:max_iterations
+        node = root
+        while !is_leaf(node)
+            node = select(node, c)
+        end
+
+        expand(node, node.state, actions)
+
+        leaf_node = node.children[node.state]
+        reward = simulate(leaf_node.state, max_depth)
+        backpropagate(leaf_node, reward)
+    end
+
+    best_child_state = argmax([child.total_reward / child.visits for child in values(root.children)])
+    return best_child_state
+end
+
+# Define your transition function and action selection function here
+
+# Example usage
+initial_state = 0
+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.
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+
+end