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This commit is contained in:
narawat lamaiin
2024-04-20 19:54:38 +07:00
parent 2d8b15e390
commit ff8b20716d
2 changed files with 88 additions and 25 deletions
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51
src/mcts copy.jl
View File

@@ -12,13 +12,21 @@ using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- #
"""
TODO\n
[] update docstring
"""
struct MCTSNode{T}
state::T
visits::Int
total_reward::Float64
children::Dict{T, MCTSNode}
state::T
visits::Int
total_reward::Float64
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.

62
src/mcts.jl
View File

@@ -12,13 +12,21 @@ using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- #
"""
TODO\n
[] update docstring
"""
struct MCTSNode{T}
state::T
visits::Int
total_reward::Float64
children::Dict{T, MCTSNode}
state::T
visits::Int
total_reward::Float64
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)
node = select(node, c)
while !isLeaf(node)
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.