update
This commit is contained in:
@@ -51,7 +51,7 @@ julia>
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# TODO
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[] update docstring
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[x] implement the function
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[PENDING] implement the function
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[] implement RAG to pull similar experience
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[] use iterative prompting to ensure JSON format
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@@ -143,8 +143,8 @@ function decisionMaker(a::T1, state::T2) where {T1<:agent, T2<:AbstractDict}
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)
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)
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thought = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
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result = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
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thought = result[:response][:text]
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return thought
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end
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@@ -288,7 +288,7 @@ end
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function conversation(a::T, userinput::Dict) where {T<:agent}
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"""
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[] update document
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[x] MCTS() for planning
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[PENDING] MCTS() for planning
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"""
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# "newtopic" command to delete chat history
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if userinput[:text] == "newtopic"
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332
src/mcts.jl
332
src/mcts.jl
@@ -15,43 +15,38 @@ using ..type
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""" a node for MCTS search tree
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Arguments\n
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-----
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state::T
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a state of a game. Can be a Dict or something else.
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For example:
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state = Dict(
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:info=> Dict(), # keyword info
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:thoughtHistory=> Dict(
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:question=> _,
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:thought_1=> _,
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:action_1=> _,
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:observation_1=> _,
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:thought_2=> _,
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...
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)
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)
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visits::Integer
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number of time the game visits this state
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stateValue::Float64
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state value
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# Arguments
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- `state::T`
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a state of a game. Can be a Dict or something else.
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For example:
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state = Dict(
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:info=> Dict(), # keyword info
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:thoughtHistory=> Dict(
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:question=> _,
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:thought_1=> _,
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:action_1=> _,
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:observation_1=> _,
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:thought_2=> _,
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...
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)
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)
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- `visits::Integer `
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number of time the game visits this state
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- `stateValue::Float64`
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state value
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Return\n
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-----
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# Return
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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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# Example
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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 the function
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# TODO
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[] update docstring
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[x] implement the function
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Signature\n
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-----
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# Signature
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"""
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struct MCTSNode{T<:AbstractDict}
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state::T
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@@ -62,28 +57,23 @@ end
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""" Select a node based on UCT score
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Arguments\n
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-----
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node::MCTSNode
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mcts node
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w::Float64
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exploration weight
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Return\n
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-----
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# Arguments
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- `node::MCTSNode`
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mcts node
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- `w::Float64`
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exploration weight
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# Return
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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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# Example
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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] check childNode.total_reward w/ LATS paper. Which value total_reward representing
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# TODO
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[] update docstring
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[DONE] check childNode.total_reward w/ LATS paper. Which value total_reward representing
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Signature\n
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-----
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# Signature
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"""
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function select(node::MCTSNode, w::Float64)
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max_uct = -Inf
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@@ -103,31 +93,26 @@ end
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""" Expand selected node
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Arguments\n
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-----
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node::MCTSNode
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MCTS node
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state::T
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a state of a game. Can be a Dict or something else.
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decisionMaker::Function
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# Arguments
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- `node::MCTSNode`
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MCTS node
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- `state::T`
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a state of a game. Can be a Dict or something else.
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- `decisionMaker::Function`
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Return\n
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-----
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# Return
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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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# Example
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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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[x] implement the function
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# TODO
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- [] update docstring
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- [WORKING] implement the function
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Signature\n
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-----
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# Signature
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"""
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function expand(a::T1, node::MCTSNode, state::T2, decisionMaker::Function, stateValueEstimator::Function;
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n::Integer=3) where {T1<:agent, T2<:AbstractDict}
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@@ -149,26 +134,21 @@ 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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# Arguments
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# Return
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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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# Example
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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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[] reward only comes at terminal state
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# TODO
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- [] update docstring
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- [] implement the function
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- [] reward only comes at terminal state
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Signature\n
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-----
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# Signature
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"""
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function simulate(state::T, max_depth::Int) where {T<:AbstractDict}
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total_reward = 0.0
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@@ -186,25 +166,20 @@ 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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# Arguments
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# Return
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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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# Example
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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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# TODO
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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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# Signature
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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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@@ -219,79 +194,61 @@ 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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# Arguments
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# Return
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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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# Example
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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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# TODO
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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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# Signature
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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 of a tree
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"""
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Arguments\n
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-----
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node::MCTSNode
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node of a tree
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Return\n
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-----
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result::Bool
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true if the node is a leaf node of a tree otherwise false
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# Arguments
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# Return
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Example\n
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-----
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```jldoctest
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julia> using
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```
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# Example
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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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# TODO
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- [] update docstring
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- [x] implement the function
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Signature\n
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-----
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# Signature
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"""
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isleaf(node::MCTSNode)::Bool = isempty(node.children)
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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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# Arguments
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# Return
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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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# Example
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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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# TODO
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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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# Signature
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"""
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function executeLLMFunction()
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@@ -303,42 +260,37 @@ end
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# ------------------------------------------------------------------------------------------------ #
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""" Search the best action to take for a given state and task
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Arguments\n
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-----
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a::agent
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one of Yiem's agents
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# Arguments
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- `a::agent`
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one of Yiem's agents
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- `initial state`
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initial state
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initial state
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decisionMaker::Function
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decide what action to take
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stateValueEstimator::Function
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assess the value of the state
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reflector::Function
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generate lesson from trajectory and reward
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isterminal::Function
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determine whether a given state is a terminal state
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n::Integer
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how many times action will be sampled from decisionMaker
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w::Float64
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exploration weight
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Return\n
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-----
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plan::Vector{Dict}
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best plan
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- `decisionMaker::Function`
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decide what action to take
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- `stateValueEstimator::Function`
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assess the value of the state
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- `reflector::Function`
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generate lesson from trajectory and reward
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- `isterminal::Function`
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determine whether a given state is a terminal state
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- `n::Integer`
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how many times action will be sampled from decisionMaker
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- `w::Float64`
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exploration weight
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# Return
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- `plan::Vector{Dict}`
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best plan
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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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# Example
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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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# TODO
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[] update docstring
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Signature\n
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-----
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# Signature
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"""
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function runMCTS(
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a::T1,
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@@ -293,7 +293,7 @@ end
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TODO\n
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-----
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[] update docstring
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[WORKING] implement the function
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[PENDING] implement the function
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Signature\n
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-----
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@@ -332,7 +332,7 @@ end
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TODO\n
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-----
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[] update docstring
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[WORKING] implement the function
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[TESTING] implement the function
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Signature\n
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-----
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@@ -100,10 +100,6 @@ response = YiemAgent.conversation(a, Dict(:text=> "Hello, I would like a get a b
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Reference in New Issue
Block a user