287 lines
4.8 KiB
Julia
287 lines
4.8 KiB
Julia
""" To implement a Monte Carlo Tree Search (MCTS) algorithm in Julia with the UCT (Upper Confidence
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Bound for Trees) selection function, you can follow the steps below: Define the necessary types
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and functions for the MCTS algorithm:
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"""
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module MCTS
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# export
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using Dates, UUIDs, DataStructures, JSON3, Random
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using GeneralUtils
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# ---------------------------------------------- 100 --------------------------------------------- #
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[] implement the function
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Signature\n
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-----
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"""
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struct MCTSNode{T}
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state::T
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visits::Int
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total_reward::Float64
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children::Dict{T, MCTSNode}
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end
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[WORKING] check child_node.total_reward w/ LATS paper. Which value total_reward representing
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Signature\n
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-----
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"""
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function select(node::MCTSNode, c::Float64)
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max_uct = -Inf
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selected_node = nothing
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for (child_state, child_node) in node.children
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uct_value = child_node.total_reward / child_node.visits +
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c * sqrt(log(node.visits) / child_node.visits)
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if uct_value > max_uct
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max_uct = uct_value
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selected_node = child_node
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end
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end
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return selected_node
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end
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[] implement the function
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Signature\n
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-----
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"""
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function expand(node::MCTSNode, state::T, actions::Vector{T})
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for action in actions
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new_state = transition(node.state, action) # Implement your transition function
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if new_state ∉ keys(node.children)
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node.children[new_state] = MCTSNode(new_state, 0, 0.0, Dict{T, MCTSNode}())
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end
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end
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end
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[] implement the function
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Signature\n
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-----
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"""
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function simulate(state::T, max_depth::Int)
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total_reward = 0.0
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for _ in 1:max_depth
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action = select_action(state) # Implement your action selection function
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state, reward = transition(state, action) # Implement your transition function
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total_reward += reward
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end
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return total_reward
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end
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[] implement the function
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Signature\n
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-----
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"""
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function backpropagate(node::MCTSNode, reward::Float64)
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node.visits += 1
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node.total_reward += reward
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if !isempty(node.children)
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best_child = argmax([child.total_reward / child.visits for child in values(node.children)])
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backpropagate(node.children[best_child], -reward)
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end
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end
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[] implement the function
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Signature\n
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-----
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"""
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function transition(state, action)
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end
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""" Check whether a node is a leaf node
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Arguments\n
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-----
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Return\n
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-----
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a task represent an agent
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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[DONE] implement isLeaf()
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Signature\n
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-----
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"""
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isLeaf(node::MCTSNode)::Bool = isempty(node.children)
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# ------------------------------------------------------------------------------------------------ #
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# Create a complete example using the defined MCTS functions #
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# ------------------------------------------------------------------------------------------------ #
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"""
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Arguments\n
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-----
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Return\n
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-----
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Example\n
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-----
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```jldoctest
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julia>
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```
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TODO\n
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-----
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[] update docstring
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Signature\n
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-----
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"""
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function run_mcts(initial_state, actions, max_iterations::Int, max_depth::Int, w::Float64)
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root = MCTSNode(initial_state, 0, 0.0, Dict())
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for _ in 1:max_iterations
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node = root
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while !isLeaf(node)
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node = select(node, w)
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end
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expand(node, node.state, actions)
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leaf_node = node.children[node.state]
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reward = simulate(leaf_node.state, max_depth)
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backpropagate(leaf_node, reward)
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end
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best_child_state = argmax([child.total_reward / child.visits for child in values(root.children)])
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return best_child_state
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end
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# Define your transition function and action selection function here
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# Example usage
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initial_state = 0
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actions = [-1, 0, 1]
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best_action = run_mcts(initial_state, actions, 1000, 10, 1.0)
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println("Best action to take: ", best_action)
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end |