current work on making operator level maximization as opposed to just the planner level.

julia_code/BellmanResidualMinimization.jl

@@ -69,8 +69,8 @@ end
 # ╔═╡ 5b45b29e-f0f4-41e9-91e7-d444687feb4e
 #implement survival function
 function survival(
-		stocks::Array
+		stocks::Array{Float32}
-		,debris::Array
+		,debris::Array{Float32}
 		,physical_model::BasicModel
 	)	
     return exp.(
@@ -82,9 +82,9 @@ end
 # ╔═╡ 152f3a3c-a565-41bb-8e59-6ab0d2315ffb
 #debris evolution 
 function H(
-		stocks::Array
+		stocks::Array{Float32}
-		,debris::Array
+		,debris::Array{Float32}
-		,launches::Array
+		,launches::Array{Float32}
 		, physical_model::BasicModel
 	)
     #get changes in debris from natural dynamics
@@ -103,9 +103,9 @@ end
 # ╔═╡ 25ac9438-2b1d-4f6b-9ff1-1695e1d52b51
 #stock update rules
 function G(
-		stocks::Array
+		stocks::Array{Float32}
-		,debris::Array
+		,debris::Array{Float32}
-		,launches::Array
+		,launches::Array{Float32}
 		, physical_model::BasicModel
 )
     return LinearAlgebra.diagm(survival(stocks,debris,physical_model) .- physical_model.decay_rate)*stocks + launches
@@ -117,42 +117,44 @@ md"""
 """
 
 # ╔═╡ f7aabe43-9a2c-4fe0-8099-c29cdf66566c
-function value_function_generator(number_params=10)
+function value_function_generator(number_params=32)
 	return Flux.Chain(
 	    Flux.Parallel(vcat
 		        #parallel joins together stocks and debris, after a little bit of preprocessing
 		        ,Flux.Chain(
-					Flux.Dense(N_constellations, N_states*2,Flux.relu)
+					Flux.Dense(N_constellations, number_params*2,Flux.relu)
-		            ,Flux.Dense(N_states*2, N_states*2,Flux.σ)
+		            ,Flux.Dense(number_params*2, number_params*2,Flux.σ)
 		        )
 		        ,Flux.Chain(
-					Flux.Dense(N_debris, N_states,Flux.relu)
+					Flux.Dense(N_debris, number_params,Flux.relu)
-		            ,Flux.Dense(N_states, N_states,Flux.σ)
+		            ,Flux.Dense(number_params, number_params,Flux.σ)
 				)
 			)
 		    #Apply some transformations to the preprocessed data.
-			,Flux.Dense(N_states*3,number_params,Flux.σ)
+			,Flux.Dense(number_params*3,number_params,Flux.σ)
+			,Flux.Dense(number_params,number_params,Flux.σ)
 			,Flux.Dense(number_params,1)
 		)
 
 end
 
 # ╔═╡ d816b252-bdca-44ba-ac5c-cb21163a1e9a
-function policy_function_generator(number_params=10)
+function planner_policy_function_generator(number_params=32)
 	return Flux.Chain(
 	    Flux.Parallel(vcat
 		        #parallel joins together stocks and debris
 		        ,Flux.Chain(
-					Flux.Dense(N_constellations, N_states*2,Flux.relu)
+					Flux.Dense(N_constellations, number_params*2,Flux.relu)
-		            ,Flux.Dense(N_states*2, N_states*2,Flux.σ)
+		            ,Flux.Dense(number_params*2, number_params*2,Flux.σ)
 		        )
 		        ,Flux.Chain(
-					Flux.Dense(N_debris, N_states,Flux.relu)
+					Flux.Dense(N_debris, number_params,Flux.relu)
-		            ,Flux.Dense(N_states, N_states)
+		            ,Flux.Dense(number_params, number_params)
 				)
 			)
 		    #Apply some transformations
-			,Flux.Dense(N_states*3,number_params,Flux.σ)
+			,Flux.Dense(number_params*3,number_params,Flux.σ)
+			,Flux.Dense(number_params,number_params,Flux.σ)
 			,Flux.Dense(number_params,N_constellations,Flux.relu)
 		)
 
@@ -176,6 +178,12 @@ begin
 	Flux.@functor Split
 	(m::Split)(x::AbstractArray) = tuple(map(f -> f(x), m.paths))
 
+	struct Duplicate
+	  n::Int
+	end
+	Flux.@functor Duplicate
+	(m::Duplicate)(x::Tuple) = [x for i=1:m.n]
+	
 	### TESTING ###
 
 
@@ -187,21 +195,55 @@ begin
 
 end
 
+# ╔═╡ 6a3b5f7a-a535-450f-8c5f-19bdcc280146
+function operators_policy_function_generator(number_params=32)
+	function f()
+		return Flux.Chain(
+	    Flux.Parallel(vcat
+		        #parallel joins together stocks and debris
+		        ,Flux.Chain(
+					Flux.Dense(N_constellations, number_params,Flux.relu)
+		            ,Flux.Dense(number_params, number_params,Flux.σ)
+		        )
+		        ,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.σ)
+			,Flux.Dense(number_params,number_params,Flux.σ)
+			,Flux.Dense(number_params,1,Flux.relu)
+		)
+	end
+
+	a = [f() for i=1:N_constellations]
+
+	Flux.Chain(
+		Duplicate(N_constellations)
+		
+	)
+
+end
+
+# ╔═╡ 0ba48a75-36db-4003-95c0-2329d4fb29c5
+Duplicate(2)(([1.0f0 2],[3f0]))
+
 # ╔═╡ 340da189-f443-4376-a82d-7699a21ab7a2
 abstract type EconomicParameters end
 
 # ╔═╡ 206ac4cc-5102-4381-ad8a-777b02dc4d5a
 begin #basic linear model
 	struct EconModel1 <: EconomicParameters
-		β::Real
+		β::Float32
-		payoff_array::Array{Real}
+		payoff_array::Array{Float32}
-		policy_costs::Array{Real}
+		policy_costs::Array{Float32}
 	end
 
 	function payoff1(
-	s::Vector
+	s::Vector{Float32}
-	,d::Vector
+	,d::Vector{Float32}
-	,a::Vector
+	,a::Vector{Float32}
 	,em::EconModel1
 )
 	return em.payoff_array*s - em.policy_costs*a
@@ -212,16 +254,16 @@ end
 # ╔═╡ eebb8706-a431-4fd1-b7a5-40f07a63d5cb
 begin #basic CES model
 		struct CESParams <: EconomicParameters
-			β::Real
+			β::Float32
-			r::Real #elasticity of subsititution
+			r::Float32 #elasticity of subsititution
-			payoff_array::Array{Real}
+			payoff_array::Array{Float32}
-			policy_costs::Array{Real}
+			policy_costs::Array{Float32}
-			debris_costs::Array{Real}
+			debris_costs::Array{Float32}
 		end
 	function CES_with_debris(
-		s::Vector
+		s::Vector{Float32}
-		,d::Vector
+		,d::Vector{Float32}
-		,a::Vector
+		,a::Vector{Float32}
 		,em::CESParams
 	)
 		return (em.payoff_array*(s.^em.r) - em.debris_costs*(d.^em.r)).^(1/em.r) - em.policy_costs*a
@@ -250,21 +292,25 @@ md"""
 """
 
 # ╔═╡ 65e0b1fa-d5e1-4ff6-8736-c9d6b5f40150
-em1 = EconModel1(0.95, [1 0 0 0], [5 0 0 0])
+begin
-#=
+	em1_a = EconModel1(0.95, [1.0 0 0 0], [5 0 0 0])
-This is the most basic profit model
+	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])
-You earn 1 per operating satellite and it costs 5 per launch.
+	#=
-Only interaction is through debris.
+	This is the most basic profit model
-=#
+	
+	You earn 1 per operating satellite and it costs 5 per launch.
+	Only interaction is through debris.
+	=#
+end
 
 # ╔═╡ 19ccfc3a-6dbb-4c64-bf03-e2e219ef0efe
 begin
-	em2_a = EconModel1(0.95, [1 -0.02 -0.02 0], [5 0 0 0])
+	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 5 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 5 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 5])
+	em2_d = EconModel1(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.
@@ -287,13 +333,13 @@ md"""
 """
 
 # ╔═╡ fb6aacff-c42d-4ec1-88cb-5ce1b2e8874f
-policy = policy_function_generator();
+policy = planner_policy_function_generator();
 
 # ╔═╡ 41271ab4-1ec7-431f-9efb-0f7c3da2d8b4
 #Constellation level loss function
 function Ξ(
-	s::Vector
+	s::Vector{Float32}
-	,d::Vector
+	,d::Vector{Float32}
 	, physical_model::PhysicalParameters
 	,co::ConstellationOperator
 )
@@ -322,14 +368,17 @@ end
 
 # ╔═╡ 43b99708-0052-4b78-886c-92ac2b532f29
 begin #testing
-	s1 = ones(N_constellations)
+	s1 = ones(Float32,N_constellations)
-	d1 = ones(N_debris)
+	d1 = ones(Float32,N_debris)
 	Ξ(s1,d1,bm,operators[1])
 end
 
 # ╔═╡ dff642d9-ec5a-4fed-a059-6c07760a3a58
 #planner's loss function
-function planners_loss(s,d) 
+function planners_loss(
+	s::Vector{Float32}
+	,d::Vector{Float32}
+) 
 	l = 0.0
 	for co in operators
 		l += Ξ(s,d,bm,co)
@@ -340,13 +389,13 @@ end
 
 # ╔═╡ 5abebc1a-370c-4f5f-8826-dc0b143d5166
 md"""
-## Constructing data
+## Constructing data and training
 """
 
 # ╔═╡ a20959be-65e4-4b69-9521-503bc59f0854
 begin
 	N=200 #increase later
-	data = [(rand(1:500, N_constellations),rand(1:500, N_debris)) for n=1:N]
+	data = [(rand(1:500f0, N_constellations),rand(1:500f0, N_debris)) for n=1:N]
 end
 
 # ╔═╡ 6bf8d29a-7990-4e91-86e6-d9894ed3db27
@@ -355,7 +404,7 @@ ADAM = Flux.Optimise.ADAM(0.1)
 
 # ╔═╡ e7ee1a0f-ab9b-439e-a7be-4a6d3b8f160d
 begin
-	accum1 = 0.0
+	local accum1 = 0.0
 	for d in data
 		accum1 += planners_loss(d...)
 	end
@@ -364,8 +413,8 @@ end
 
 # ╔═╡ 74f5fde3-0593-46fc-a688-f1db7ab28c64
 # Social planners problem
-for epoch in 1:200
+for epoch in 1:20
-	data1 = [(rand(1:500, N_constellations),rand(1:500, N_debris)) for n=1:N]
+	data1 = [(rand(1:500f0, N_constellations),rand(1:500f0, N_debris)) for n=1:N]
 	
 	#train the social planner's policy funciton
 	Flux.Optimise.train!(planners_loss, params(policy), data1, ADAM)
@@ -378,7 +427,7 @@ end
 
 # ╔═╡ 02f3fe78-e7a7-453f-9ddf-acddf08d8676
 begin
-	accum = 0.0
+	local accum = 0.0
 	for d in data
 		accum += planners_loss(d...)
 		
@@ -391,7 +440,7 @@ policy(data[3])
 
 # ╔═╡ 14e61097-f28f-4029-b6b4-5fb119620fc3
 begin
-	n=1
+	n=15
 	
 	[operators[1].value(data[n])
 	,operators[2].value(data[n])
@@ -982,12 +1031,14 @@ uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
 # ╠═9fa41b7c-1923-4c1e-bfc6-20ce4a1a2ede
 # ╟─90446134-4e45-471c-857d-4e165e51937a
 # ╠═5b45b29e-f0f4-41e9-91e7-d444687feb4e
-# ╟─152f3a3c-a565-41bb-8e59-6ab0d2315ffb
+# ╠═152f3a3c-a565-41bb-8e59-6ab0d2315ffb
-# ╟─25ac9438-2b1d-4f6b-9ff1-1695e1d52b51
+# ╠═25ac9438-2b1d-4f6b-9ff1-1695e1d52b51
 # ╠═29ff1777-d276-4e8f-8582-4ca191f2e2ff
-# ╟─f7aabe43-9a2c-4fe0-8099-c29cdf66566c
+# ╠═f7aabe43-9a2c-4fe0-8099-c29cdf66566c
-# ╟─d816b252-bdca-44ba-ac5c-cb21163a1e9a
+# ╠═d816b252-bdca-44ba-ac5c-cb21163a1e9a
+# ╠═6a3b5f7a-a535-450f-8c5f-19bdcc280146
 # ╠═95bfc9d8-8427-41d6-9f0f-f155296eef91
+# ╠═0ba48a75-36db-4003-95c0-2329d4fb29c5
 # ╠═340da189-f443-4376-a82d-7699a21ab7a2
 # ╠═206ac4cc-5102-4381-ad8a-777b02dc4d5a
 # ╠═eebb8706-a431-4fd1-b7a5-40f07a63d5cb
@@ -1003,7 +1054,7 @@ uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
 # ╠═cd55e232-493d-4849-8bd7-b0ba85e21bab
 # ╠═fb6aacff-c42d-4ec1-88cb-5ce1b2e8874f
 # ╠═f30904a7-5caa-449a-a5bd-f2aa78777a9a
-# ╟─5abebc1a-370c-4f5f-8826-dc0b143d5166
+# ╠═5abebc1a-370c-4f5f-8826-dc0b143d5166
 # ╠═a20959be-65e4-4b69-9521-503bc59f0854
 # ╠═6bf8d29a-7990-4e91-86e6-d9894ed3db27
 # ╠═e7ee1a0f-ab9b-439e-a7be-4a6d3b8f160d