working convergence, with a saved model

4 years ago · 333bc5000c
parent be47bfc2f1
commit 333bc5000c
2 changed files with 340 additions and 69 deletions
--- a/julia_code/2021-11-17T08:45:09.302_planner.bson
+++ b/julia_code/2021-11-17T08:45:09.302_planner.bson
--- a/julia_code/BellmanResidual_Operators.jl
+++ b/julia_code/BellmanResidual_Operators.jl
@ -7,6 +7,17 @@ using InteractiveUtils
 # ╔═╡ 0829eb90-1d74-46b6-80ec-482a2b71c6fe
 using PlutoUI, Flux,LinearAlgebra

+# ╔═╡ f360390b-1d15-4443-aad6-a5800a6ba776
+using BSON: @save,@load
+
+# ╔═╡ 9a1ebef5-ed53-4e0b-89e7-20a20819b032
+using Dates,BSON
+
+# ╔═╡ fe446c7e-ed8c-4a0b-8bc9-024806f9f352
+md"""
+$(Dates.now())
+"""
+
 # ╔═╡ 66f0e667-d722-4e1e-807b-84a39cbc41b1
 md"""
 # Bellman Residual Minimization of Operators
@ -133,9 +144,10 @@ begin
 	    return LinearAlgebra.diagm(survival(stocks,debris,self.physical_model) .- self.physical_model.decay_rate)*stocks + launches
 	end
 end
+#TODO: investigate combining the stocks and debris using some sort of state struct <: State type. Maybe combine with the physical model?

 # ╔═╡ 6bf01e30-d054-4a98-9e0d-58d7da4e5524
-begin
+begin # build the transition models
 	H = BasicDebrisEvolution(bm)
 	G = BasicStockEvolution(bm)
 end;
@ -189,8 +201,53 @@ function planner_policy_function_generator(number_params=32)

 end

+# ╔═╡ e4e2f50e-05fc-42da-a6d3-d62c4f901d84
+function operator_policy_function_generator(number_params=32)
+	return Flux.Chain(
+	    Flux.Parallel(vcat
+		        #parallel joins together stocks and debris
+		        ,Flux.Chain(
+					Flux.Dense(N_constellations, number_params*2,Flux.relu)
+		            ,Flux.Dense(number_params*2, number_params*2,Flux.tanh)
+		        )
+		        ,Flux.Chain(
+					Flux.Dense(N_debris, number_params,Flux.relu)
+		            ,Flux.Dense(number_params, number_params)
+				)
+			)
+		    #Apply some transformations
+			,Flux.Dense(number_params*3,number_params,Flux.tanh)
+			,Flux.Dense(number_params,number_params,Flux.tanh)
+			,Flux.Dense(number_params,1,x -> Flux.relu.(sinh.(x)))
+		)
+
+end
+
 # ╔═╡ ae6681e0-c796-4145-a313-75f74b4993ad
 #add branch generator
+begin #move to module
+	#= TupleDuplicator
+	This is used to create a tuple of size n with deepcopies of any object x
+	=#
+	struct TupleDuplicator
+		n::Int
+	end
+	(f::TupleDuplicator)(x) = tuple([deepcopy(x) for i=1:f.n]...)
+
+	#=
+	This generates a policy function full of branches with the properly scaled sides
+	=#
+	struct BranchGenerator
+		n::UInt8 
+		#limit to 2^8 operators
+	end
+	function (b::BranchGenerator)(branch::Flux.Chain,join_fn::Function) 
+		# used to deepcopy the branches and duplicate the inputs in the returned chain
+		f = TupleDuplicator(b.n)
+		
+		return Flux.Chain(f,Flux.Parallel(join_fn,f(branch)))
+	end
+end

 # ╔═╡ bbca5143-f314-40ea-a20e-8a043272e362
 md"""
@ -201,13 +258,14 @@ md"""
 abstract type EconomicModel end

 # ╔═╡ 206ac4cc-5102-4381-ad8a-777b02dc4d5a
-begin #basic linear model
-	struct EconModel1 <: EconomicModel
+begin 
+	#basic linear model
+	struct LinearModel <: EconomicModel
 		β::Float32
 		payoff_array::Array{Float32}
 		policy_costs::Array{Float32}
 	end
-	function (em::EconModel1)(
+	function (em::LinearModel)(
 		s::Vector{Float32}
 		,d::Vector{Float32}
 		,a::Vector{Float32}
@ -217,20 +275,47 @@ begin #basic linear model
 end

 # ╔═╡ eebb8706-a431-4fd1-b7a5-40f07a63d5cb
-begin #basic CES model
-	struct CESParams <: EconomicModel
+begin 
+	#basic CES model
+	struct CES <: EconomicModel
 		β::Float32
 		r::Float32 #elasticity of subsititution
 		payoff_array::Array{Float32}
 		policy_costs::Array{Float32}
 		debris_costs::Array{Float32}
 	end
-	function (em::CESParams)(
+	function (em::CES)(
 		s::Vector{Float32}
 		,d::Vector{Float32}
 		,a::Vector{Float32}
 		)
-		return (em.payoff_array*(s.^em.r) - em.debris_costs*(d.^em.r)).^(1/em.r) - em.policy_costs*a
+
+		#issue here with multiplication
+		r1 = em.payoff_array .* (s.^em.r) 
+		r2 = - em.debris_costs .* (d.^em.r) 
+		r3 = - em.policy_costs .* (a.^em.r)
+		return (r1 + r2 + r3) .^ (1/em.r)
+
+	end
+
+
+	#basic CRRA
+	struct CRRA <: EconomicModel
+		β::Float32
+		σ::Float32 #elasticity of subsititution
+		payoff_array::Array{Float32}
+		policy_costs::Array{Float32}
+		debris_costs::Array{Float32}
+	end
+	function (em::CRRA)(
+		s::Vector{Float32}
+		,d::Vector{Float32}
+		,a::Vector{Float32}
+		)
+		#issue here
+		core = (em.payoff_array*s - em.debris_costs*d - em.policy_costs).^(1 - em.σ)
+
+		return (core-1)/(1-em.σ)
 	end
 end

@ -241,10 +326,10 @@ md"""

 # ╔═╡ 65e0b1fa-d5e1-4ff6-8736-c9d6b5f40150
 begin
-	em1_a = EconModel1(0.95, [1.0 0 0 0], [5 0 0 0])
-	em1_b = EconModel1(0.95, [0 1.0 0 0], [0 5 0 0])
-	em1_c = EconModel1(0.95, [0 0 1.0 0], [0 0 5 0])
-	em1_d = EconModel1(0.95, [0 0 0 1.0], [0 0 0 5])
+	em1_a = LinearModel(0.95, [1.0 0 0 0], [5 0 0 0])
+	em1_b = LinearModel(0.95, [0 1.0 0 0], [0 5 0 0])
+	em1_c = LinearModel(0.95, [0 0 1.0 0], [0 0 5 0])
+	em1_d = LinearModel(0.95, [0 0 0 1.0], [0 0 0 5])
 	#=
 	This is the most basic profit model
 	
@ -255,10 +340,10 @@ end

 # ╔═╡ 19ccfc3a-6dbb-4c64-bf03-e2e219ef0efe
 begin
-	em2_a = EconModel1(0.95, [1 -0.02 -0.02 0], [5.0 0 0 0])
-	em2_b = EconModel1(0.95, [-0.02 1 -0.02 0], [0.0 5 0 0])
-	em2_c = EconModel1(0.95, [0 -0.02 1 -0.02], [0.0 0 5 0])
-	em2_d = EconModel1(0.95, [0 -0.02 -0.02 1], [0.0 0 0 5])
+	em2_a = LinearModel(0.95, [1 -0.02 -0.02 0], [5.0 0 0 0])
+	em2_b = LinearModel(0.95, [-0.02 1 -0.02 0], [0.0 5 0 0])
+	em2_c = LinearModel(0.95, [0 -0.02 1 -0.02], [0.0 0 5 0])
+	em2_d = LinearModel(0.95, [0 -0.02 -0.02 1], [0.0 0 0 5])
 	#=
 	This is a simple addition to the basic model, where you lose some benefit based 
 	the size of your competitor's satellites.
@ -267,18 +352,32 @@ begin
 end

 # ╔═╡ dc614254-c211-4552-b985-03020bfc5ab3
-em3 = CESParams(0.95,0.6,[1 0 0 0], [5 0 0 0], Vector([0.002]))
-#=
-This is a variation on a CES model.
+begin
+	em3_a = CES(0.95,0.6,[1 0 0 0], [5 0 0 0], Vector([0.002]))
+	em3_b = CES(0.95,0.6,[0 1 0 0], [0 5 0 0], Vector([0.002]))
+	em3_c = CES(0.95,0.6,[0 0 1 0], [0 0 5 0], Vector([0.002]))
+	em3_d = CES(0.95,0.6,[0 0 0 1], [0 0 0 5], Vector([0.002]))
+	#=
+	This is a variation on a CES model.
+	
+	The model is CES the relationship between payoffs and debris.
+	In this particular specification, the only interaction is in debris
+	=#
+end

-The model is CES the relationship between payoffs and debris.
-In this particular specification, the only interaction is in debris
-=#
+# ╔═╡ 128ab84e-9682-4c4e-a301-1e27d9c199a4
+begin
+	em4_a = CRRA(0.95,0.6,[1 0 0 0], [5 0 0 0], Vector([0.002]))
+	em4_b = CRRA(0.95,0.6,[0 1 0 0], [0 5 0 0], Vector([0.002]))
+	em4_c = CRRA(0.95,0.6,[0 0 1 0], [0 0 5 0], Vector([0.002]))
+	em4_d = CRRA(0.95,0.6,[0 0 0 1], [0 0 0 5], Vector([0.002]))
+end
+
+# ╔═╡ 52eb1934-77b1-4da3-a36a-afb951f76784
+#TODO: create a generator for economic models
+
+# ╔═╡ 717a07a4-5254-40aa-8244-1a2010fedec8

-# ╔═╡ 4452ab2c-813b-4b7b-8cb3-388ec507a24c
-md"""
-## Setup Operators Policy Functions
-"""

 # ╔═╡ 5946daa3-4608-43f3-8933-dd3eb3f4541c
 md"""
@ -292,6 +391,15 @@ begin #testing data
 	
 end

+# ╔═╡ aa079b60-af16-4824-8a1d-b0c68a0cacbc
+begin
+	#test economic models
+	em1_a(s1,d1,s1)
+	em2_a(s1,d1,s1)
+	#em3_a(s1,d1,s1) #ERROR
+	#em4_a(s1,d1,s1) #ERROR
+end
+
 # ╔═╡ 41271ab4-1ec7-431f-9efb-0f7c3da2d8b4
 #Constellation operator loss function
 function Ξ(
@ -317,7 +425,7 @@ function Ξ(
 	return sum(bellman_residuals.^2, maximization_condition)
 end

-# ╔═╡ 89258b2e-cee5-4cbd-a42e-21301b7a2549
+# ╔═╡ 382b6dee-5fa7-4ca1-b804-4b50b0fb65a7
 begin
 	#=
 	This struct organizes information about a given constellation operator
@ -339,27 +447,49 @@ begin
 	function (operator::ConstellationOperatorLoss)(
 		s::Vector{Float32}
 		,d::Vector{Float32}
-		; #set default policy to the one held by the ConstellationOperator
-		policy::Flux.Chain = operator.collected_policies
 	)
 		

 		#get actions
-		a = policy((s,d))
+		a = operator.collected_policies((s,d))
 	
 		#get updated stocks and debris
-		s′ = stocks_transition(s ,d ,a)
-		d′ = debris_transition(s ,d ,a)
+		s′ = operator.stocks_transition(s ,d ,a)
+		d′ = operator.debris_transition(s ,d ,a)
 	
 		
 		bellman_residuals = operator.operator_value_fn((s,d)) - operator.econ_model(s,d,a) - operator.econ_model.β * operator.operator_value_fn((s′,d′))
 		
-		maximization_condition = - operator.econ_model(s,d,a,co.econ_params) - co.econ_params.β*co.value((s′,d′))
+		maximization_condition = - operator.econ_model(s ,d ,a) - operator.econ_model.β * operator.operator_value_fn((s′,d′))
 	
-		return sum(bellman_residuals.^2, maximization_condition)
+		return Flux.mae(bellman_residuals.^2 ,maximization_condition)
 	end
+	
+	function (operator::ConstellationOperatorLoss)(
+		s::Vector{Float32}
+		,d::Vector{Float32}
+		,policy::Flux.Chain #allow for another policy to be subsituted
+	)
+		

+		#get actions
+		a = policy((s,d))
+	
+		#get updated stocks and debris
+		s′ = operator.stocks_transition(s ,d ,a)
+		d′ = operator.debris_transition(s ,d ,a)
+	
+		
+		bellman_residuals = operator.operator_value_fn((s,d)) - operator.econ_model(s,d,a) - operator.econ_model.β * operator.operator_value_fn((s′,d′))
+		
+		maximization_condition = - operator.econ_model(s ,d ,a) - operator.econ_model.β * operator.operator_value_fn((s′,d′))
+	
+		return Flux.mae(bellman_residuals.^2 ,maximization_condition)
+	end
+end

+# ╔═╡ dc855793-d234-4835-8674-bf56aa0ad0db
+begin
 	
 	#do the same thing with the planner's problem
 	struct PlannerLoss
@ -369,22 +499,40 @@ begin
 		There is an issue with appropriately training the value functions.
 		In this case, it is not happening...
 		=#
+		β::Float32
+		
 		operators::Array{ConstellationOperatorLoss}
-		planner_policy::Flux.Chain
-		planner_params::Flux.Params
+		
+		policy::Flux.Chain
+		policy_params::Flux.Params
+		value::Flux.Chain
+		value_params::Flux.Params
+		
+		debris_transition::BasicDebrisEvolution
+		stocks_transition::BasicStockEvolution
 	end
-	function (pl::PlannerLoss)(
+	function (planner::PlannerLoss)(
 		s::Vector{Float32}
 		,d::Vector{Float32}
 	) 
-		l = 0.0
-		for co in pl.operators
-			l += co(s ,d ,pl.planner_policy)
-		end
-		return l
-	end
+		a = planner.policy((s ,d))
+		#get updated stocks and debris
+		s′ = planner.stocks_transition(s ,d ,a)
+		d′ = planner.debris_transition(s ,d ,a)
+		
+		
+		
+			#calculate the total benefit from each of the models
+		benefit = sum([ co.econ_model(s ,d ,a) for co in planner.operators])
+			#issue here with mutating. Maybe generators/list comprehensions?
+		
+		
+		bellman_residuals = planner.value((s,d)) - benefit - planner.β .* planner.value((s′,d′))

-	#TODO: Training Functions as routines
+		maximization_condition = - benefit - planner.β .* planner.value((s′,d′))
+		
+		return Flux.mae(bellman_residuals.^2 ,maximization_condition)
+	end
 end

 # ╔═╡ cd55e232-493d-4849-8bd7-b0ba85e21bab
@ -392,24 +540,80 @@ md"""
 # Set up Operators and Planner
 """

+# ╔═╡ 176981f2-b7f5-4a0f-aae0-940e1db778c7
+#=
+Setup various policy/planning functions
+=#
+begin
+	bg4 = BranchGenerator(N_constellations)
+
+	#create identically structured policiy functions
+	operators_policy = bg4(operator_policy_function_generator(),vcat)
+	planners_policy = bg4(operator_policy_function_generator(),vcat)
+
+	#create the planners_value function
+	planners_value = value_function_generator(64)
+end;
+
 # ╔═╡ 08ef7fbf-005b-40b5-acde-f42750c04cd3
 begin #setup operators
+	#Create policies
 	
-	const tops = [ 
-		ConstellationOperator(,em2_a,value_function_generator())
-		,ConstellationOperator(payoff1,em2_b,value_function_generator())
-		,ConstellationOperator(payoff1,em2_c,value_function_generator())
-		,ConstellationOperator(payoff1,em2_d,value_function_generator())
+	const operator_array = [ 
+		#first operator
+		ConstellationOperatorLoss(		
+			em2_a
+			,value_function_generator()
+			,operators_policy #this is held by all operators
+			,params(operators_policy[2][1]) #first operator gets first branch of params
+			,H
+			,G
+		)
+		,ConstellationOperatorLoss(		
+			em2_b
+			,value_function_generator()
+			,operators_policy #this is held by all operators
+			,params(operators_policy[2][2]) #first operator gets first branch of params
+			,H
+			,G
+		)
+		,ConstellationOperatorLoss(		
+			em2_c
+			,value_function_generator()
+			,operators_policy #this is held by all operators
+			,params(operators_policy[2][3]) #first operator gets first branch of params
+			,H
+			,G
+		)
+		,ConstellationOperatorLoss(		
+			em2_d
+			,value_function_generator()
+			,operators_policy #this is held by all operators
+			,params(operators_policy[2][4]) #first operator gets first branch of params
+			,H
+			,G
+		)
 	]
-	#TODO: setup operator losses
 	
-	#sanity Check time
-	@assert length(tops) == N_constellations "Mismatch in predetermined number of constellations and the number of operators initialized"
+	
+	#sanity check time
+	@assert length(operator_array) == N_constellations "Mismatch in predetermined number of constellations and the number of operators initialized"
 end

-# ╔═╡ fb6aacff-c42d-4ec1-88cb-5ce1b2e8874f
-#build out planner loss
-planner_policy = planner_policy_function_generator();
+# ╔═╡ 1eae5450-f9ef-49ec-9dcd-48c025562ecf
+pl = PlannerLoss(
+		0.95
+		,operator_array
+		,planners_policy
+		,params(planners_policy)
+		,planners_value
+		,params(planners_value)
+		,H
+		,G
+)
+
+# ╔═╡ f68e2322-937d-44ce-8aa9-757b27b00603
+pl(s1,d1)

 # ╔═╡ 5abebc1a-370c-4f5f-8826-dc0b143d5166
 md"""
@ -418,13 +622,17 @@ md"""

 # ╔═╡ a20959be-65e4-4b69-9521-503bc59f0854
 begin
-	struct DataConstructor
+	struct UniformDataConstructor
 		N::UInt64
+		satellites_bottom::Float32
+		satellites_top::Float32
+		debris_bottom::Float32
+		debris_top::Float32
 	end
-	(dc::DataConstructor)(bottom=1f0,top=500f0) = [(rand(bottom:top, N_constellations),rand(bottom:top, N_debris)) for n=1:dc.N]
+	(dc::UniformDataConstructor)() = [(rand(dc.satellites_bottom:dc.satellites_top, N_constellations),rand(dc.debris_bottom:dc.debris_top, N_debris)) for n=1:dc.N]

 	#create a data constructor
-	dc = DataConstructor(200)
+	dc = UniformDataConstructor(200,0,200,0,4000)

 	#get a bit of test data
 	data1 = dc()
@ -432,19 +640,61 @@ end

 # ╔═╡ 6bf8d29a-7990-4e91-86e6-d9894ed3db27
 #optimizer
-opt = Flux.Optimise.ADAM(0.1)
+opt = Flux.Optimise.ADAM()
+
+# ╔═╡ e6654ee2-776c-4b26-a5a3-324188062966
+pl(s1,d1)[1]
+
+# ╔═╡ 2b115364-a8ce-4eb9-a324-7ac213061b83
+begin
+	N_epoch = 6
+	data_gen = UniformDataConstructor(20_000,0,200,0,4000)
+end
+
+# ╔═╡ 0679c91b-9824-4017-83fd-4001dc8c2d54
+function error(pl,data)
+	total_error = 0.0f0
+	for d in data
+		total_error += pl(d...)
+	end
+	return total_error
+end

 # ╔═╡ 30417194-6cd1-4e10-9e25-1fa1c0761b9a
+begin
+	errors = []
+	for i in 1:N_epoch
+		data = data_gen()
+		Flux.train!(pl, pl.policy_params, data, opt)
+		Flux.train!(pl, pl.value_params, data, opt)
+		append!(errors,error(pl,data1)/200)
+	end
+end

+# ╔═╡ 7f42b201-01ec-4d63-b8de-92e735f28e4a
+errors
+#first run 7017,6562,5613,5405,5535,4462
+
+# ╔═╡ 65cf41b8-adf4-42b9-82dc-d2fbafccda81
+begin
+	model_name = "$(Dates.now())_planner.bson"
+	@save model_name pl
+end
+
+# ╔═╡ 913a0953-6d5c-4694-9a64-95c54f3398e4
+@load model_name testload

 # ╔═╡ 00000000-0000-0000-0000-000000000001
 PLUTO_PROJECT_TOML_CONTENTS = """
 [deps]
+BSON = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"

 [compat]
+BSON = "~0.3.4"
 Flux = "~0.12.8"
 PlutoUI = "~0.7.18"
 """
@ -494,6 +744,11 @@ git-tree-sha1 = "a598ecb0d717092b5539dbbe890c98bac842b072"
 uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b"
 version = "0.2.0"

+[[BSON]]
+git-tree-sha1 = "ebcd6e22d69f21249b7b8668351ebf42d6dc87a1"
+uuid = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0"
+version = "0.3.4"
+
 [[Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"

@ -1008,6 +1263,9 @@ uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"

 # ╔═╡ Cell order:
 # ╠═0829eb90-1d74-46b6-80ec-482a2b71c6fe
+# ╠═f360390b-1d15-4443-aad6-a5800a6ba776
+# ╠═9a1ebef5-ed53-4e0b-89e7-20a20819b032
+# ╠═fe446c7e-ed8c-4a0b-8bc9-024806f9f352
 # ╟─66f0e667-d722-4e1e-807b-84a39cbc41b1
 # ╟─9fa41b7c-1923-4c1e-bfc6-20ce4a1a2ede
 # ╟─0d209540-29ad-4b2f-9e91-fac2bbee47ff
@ -1015,32 +1273,45 @@ uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
 # ╠═f8779824-9d59-4458-926f-10beb3cd3866
 # ╠═152f3a3c-a565-41bb-8e59-6ab0d2315ffb
 # ╠═25ac9438-2b1d-4f6b-9ff1-1695e1d52b51
-# ╠═6bf01e30-d054-4a98-9e0d-58d7da4e5524
-# ╠═9b1c2f62-ec19-4667-8f93-f8b52750e317
+# ╟─9b1c2f62-ec19-4667-8f93-f8b52750e317
 # ╟─90446134-4e45-471c-857d-4e165e51937a
+# ╠═6bf01e30-d054-4a98-9e0d-58d7da4e5524
 # ╟─29ff1777-d276-4e8f-8582-4ca191f2e2ff
-# ╟─f7aabe43-9a2c-4fe0-8099-c29cdf66566c
-# ╟─d816b252-bdca-44ba-ac5c-cb21163a1e9a
+# ╠═f7aabe43-9a2c-4fe0-8099-c29cdf66566c
+# ╠═d816b252-bdca-44ba-ac5c-cb21163a1e9a
+# ╠═e4e2f50e-05fc-42da-a6d3-d62c4f901d84
 # ╠═ae6681e0-c796-4145-a313-75f74b4993ad
 # ╠═bbca5143-f314-40ea-a20e-8a043272e362
 # ╠═340da189-f443-4376-a82d-7699a21ab7a2
 # ╠═206ac4cc-5102-4381-ad8a-777b02dc4d5a
 # ╠═eebb8706-a431-4fd1-b7a5-40f07a63d5cb
 # ╠═b433a7ec-8264-48d6-8b95-53d2ec4bad05
-# ╠═65e0b1fa-d5e1-4ff6-8736-c9d6b5f40150
-# ╠═19ccfc3a-6dbb-4c64-bf03-e2e219ef0efe
-# ╠═dc614254-c211-4552-b985-03020bfc5ab3
-# ╠═4452ab2c-813b-4b7b-8cb3-388ec507a24c
+# ╟─65e0b1fa-d5e1-4ff6-8736-c9d6b5f40150
+# ╟─19ccfc3a-6dbb-4c64-bf03-e2e219ef0efe
+# ╟─dc614254-c211-4552-b985-03020bfc5ab3
+# ╟─128ab84e-9682-4c4e-a301-1e27d9c199a4
+# ╠═aa079b60-af16-4824-8a1d-b0c68a0cacbc
+# ╠═52eb1934-77b1-4da3-a36a-afb951f76784
+# ╠═717a07a4-5254-40aa-8244-1a2010fedec8
 # ╠═5946daa3-4608-43f3-8933-dd3eb3f4541c
 # ╠═9d4a668a-23e3-4f36-86f4-60e242caee3b
 # ╠═41271ab4-1ec7-431f-9efb-0f7c3da2d8b4
-# ╠═89258b2e-cee5-4cbd-a42e-21301b7a2549
+# ╠═382b6dee-5fa7-4ca1-b804-4b50b0fb65a7
+# ╠═dc855793-d234-4835-8674-bf56aa0ad0db
+# ╠═f68e2322-937d-44ce-8aa9-757b27b00603
 # ╠═cd55e232-493d-4849-8bd7-b0ba85e21bab
+# ╠═176981f2-b7f5-4a0f-aae0-940e1db778c7
 # ╠═08ef7fbf-005b-40b5-acde-f42750c04cd3
-# ╠═fb6aacff-c42d-4ec1-88cb-5ce1b2e8874f
+# ╠═1eae5450-f9ef-49ec-9dcd-48c025562ecf
 # ╠═5abebc1a-370c-4f5f-8826-dc0b143d5166
 # ╠═a20959be-65e4-4b69-9521-503bc59f0854
 # ╠═6bf8d29a-7990-4e91-86e6-d9894ed3db27
+# ╠═e6654ee2-776c-4b26-a5a3-324188062966
+# ╠═2b115364-a8ce-4eb9-a324-7ac213061b83
+# ╠═0679c91b-9824-4017-83fd-4001dc8c2d54
 # ╠═30417194-6cd1-4e10-9e25-1fa1c0761b9a
+# ╠═7f42b201-01ec-4d63-b8de-92e735f28e4a
+# ╠═65cf41b8-adf4-42b9-82dc-d2fbafccda81
+# ╠═913a0953-6d5c-4694-9a64-95c54f3398e4
 # ╟─00000000-0000-0000-0000-000000000001
 # ╟─00000000-0000-0000-0000-000000000002