### A Pluto.jl notebook ###
# v0.19.42

using Markdown
using InteractiveUtils

# ╔═╡ cc16838c-3b25-11ec-2489-11c3d35f26f4
using Flux,LinearAlgebra,Zygote ,PlutoUI

# ╔═╡ 458bb826-4eaf-42ca-b889-4d1c50a2ecae
using Flux.Optimise: AbstractOptimiser

# ╔═╡ 7d6afccb-c45e-4371-827a-43d588d4945f
md"""
# Actor Critic Model
This is an implementation of an optimizer for an actor critic model.

"""

# ╔═╡ a810a371-d2e4-4d4f-9cf6-1b98582c4b0f
md"""
## Physical Model
"""

# ╔═╡ 2e59f530-aeb0-4ce1-9312-e81a66520181
abstract type AbstractPhysicalModel end

# ╔═╡ c57e9967-75e8-47d2-836e-f28159dc6592
#setup physical model
struct BasicModel <: AbstractPhysicalModel
    #rate at which debris hits satellites
    debris_collision_rate::Real
    #rate at which satellites of different constellations collide
    satellite_collision_rates::Matrix{Float64}
    #rate at which debris exits orbits
    decay_rate::Real
    #rate at which satellites
    autocatalysis_rate::Real
    #ratio at which a collision between satellites produced debris
    satellite_collision_debris_ratio::Real
    #Ratio at which launches produce debris
    launch_debris_ratio::Real
end

# ╔═╡ 4153bfd6-2ec6-4025-93ba-8e6e061809e8
md"""
## Setup NeuralNets
"""

# ╔═╡ bbc7e4b0-5edb-4e5d-9512-1aaeae1bb4ec
md"""
Custom function to zero out parameter traces
"""

# ╔═╡ a305c33f-3388-4120-b036-3093c3ed6aa3
md"""
## setup Actor Critic struct and loop
"""

# ╔═╡ 0d375c34-3226-40b6-adaf-2f531c00d3ac
function zero(a::Flux.Params)
	Flux.Params([Base.zero(x) for x=a])
end

# ╔═╡ e79e4ecf-caec-4c1a-9626-0c1a8b5006ce
mutable struct ActorCritic
	#parameters
	λʷ::Real
	λᶿ::Real
	αʷ::Real
	αᶿ::Real
	αᴿ::Real
	
	#keep track of eligibility traces
	zᶿ::Flux.Params
	zʷ::Flux.Params
	
	#keep track of update rate
	R̄::AbstractFloat

	#Inside generator
	ActorCritic(
			λʷ::Real
			,λᶿ::Real
			,αʷ::Real
			,αᶿ::Real
			,αᴿ::Real
			,θ::Flux.Params
			,w::Flux.Params
			,R::Real
		) = 
		begin
			zθ = zero(θ) #custom zero handles params
			zw = zero(w) #custom zero handles params
			new(λʷ,λᶿ,αʷ,αᶿ,αᴿ,zθ,zw,R)
		end
end

# ╔═╡ 0ac4ac61-0ca2-4ed6-8855-85c9d1939afa
md"""
# Example use: Model setup
"""

# ╔═╡ 7102c12c-581b-4204-9986-741889cb03f6
#implement tranistion function
#percentage survival function
function survival(
		stocks
		,debris
		,physical_model::AbstractPhysicalModel
	)	
    return exp.(-physical_model.satellite_collision_rates*stocks .- (physical_model.debris_collision_rate*debris))
end

# ╔═╡ 283bdd91-d742-4f4b-a19b-2756ac01ce2c
#stock update rules
function G(
		stocks::Vector
		,debris::Vector
		,launches::Vector
		, physical_model::AbstractPhysicalModel
)
    return LinearAlgebra.diagm(survival(stocks,debris,physical_model) .- physical_model.decay_rate)*stocks + launches
end

# ╔═╡ 8a3f9518-ae0a-46f3-b37d-1017ce99c70d
#debris evolution 
function H(stocks,debris,launches,physical_model)
    #get changes in debris from natural dynamics
    natural_debris_dynamics = (1-physical_model.decay_rate+physical_model.autocatalysis_rate) * debris 
    
    #get changes in debris from satellite loss
    satellite_loss_debris = physical_model.satellite_collision_debris_ratio * (1 .- survival(stocks,debris,physical_model))'*stocks 
    
    #get changes in debris from launches
    launch_debris = physical_model.launch_debris_ratio*sum(launches)
    
    #return total debris level
    return natural_debris_dynamics .+ satellite_loss_debris .+ launch_debris
end


# ╔═╡ e4c8abfa-e96c-44b7-9858-af46adc1164a
#implement reward function
begin
	const payoff = 3*LinearAlgebra.I #- 0.02*ones(N_constellations,N_constellations)
	#Define the market profit function
	F(stocks,debris,launches) = (stocks  - 3.0*launches .+ (debris*-0.2))[1]
end

# ╔═╡ 42be8352-7ecf-4da4-bff0-61e328144ed1
#test
F(4,2,2)

# ╔═╡ e2135298-e280-40ac-a549-6a15f13b84ee
md"""
## Example use: Single actor.

### Model and Optimizer Parameterization
"""

# ╔═╡ 5a2dab62-985c-4689-ab48-da04981d15a0
#Model shape
begin
	const N_constellations = 1;
	const N_debris = 1;
	const N_states = N_constellations + N_debris;
end

# ╔═╡ 3f14b399-cae8-433a-8729-fc91c9b0bee9
# Launch function
launch_policy = Flux.Chain(
    Flux.Parallel(vcat
        #parallel joins together stocks and debris, along with intermediate interpretation
        ,Flux.Chain(Flux.Dense(N_constellations, N_states*2,Flux.relu)
            ,Flux.Dense(N_states*2, N_states*2,Flux.σ)
            )
        ,Flux.Chain(Flux.Dense(N_debris, N_states,Flux.relu)
            ,Flux.Dense(N_states, N_states,Flux.σ)
		)
	)
    #Apply some transformations
    ,Flux.Dense(N_states*3,128,Flux.σ)
    ,Flux.Dense(128,128,Flux.σ)
    ,Flux.Dense(128,N_constellations,Flux.relu)
	
)

# ╔═╡ 89dabcc8-7f4e-4df4-8445-eae9f50ebfea
#inspect parameters
Flux.params(launch_policy)

# ╔═╡ 15d5ce62-135d-493e-abad-3ec1f99bfd3b
#Test the function above
zero(Flux.params(launch_policy))

# ╔═╡ 2c9346eb-048d-4667-b22b-583b814ae1a3
# Launch function
value = Flux.Chain(
    Flux.Parallel(vcat
        #parallel joins together stocks and debris, along with intermediate interpretation
        ,Flux.Chain(Flux.Dense(N_constellations, N_states*2,Flux.relu)
            ,Flux.Dense(N_states*2, N_states*2,Flux.σ)
            )
        ,Flux.Chain(Flux.Dense(N_debris, N_states,Flux.relu)
            ,Flux.Dense(N_states, N_states,Flux.σ)
		)
	)
    #Apply some transformations
    ,Flux.Dense(N_states*3,128,Flux.σ)
    ,Flux.Dense(128,128,Flux.σ)
    ,Flux.Dense(128,1)
	
);

# ╔═╡ 95b9943b-8c13-4717-8239-2bae9f93ee63
#create the current actor critic optimizer
optim = ActorCritic(
	0.5
	,0.5
	,2.0
	,2.0
	,2.0
	,Flux.params(launch_policy)
	,Flux.params(value)
	,0.5
)

# ╔═╡ 43704f08-6f1a-47bb-87e3-483a529b1654
#Starting States
begin
	stock_state = ones(N_constellations);
	debris_state = ones(N_debris);
end;

# ╔═╡ 6f3649c1-2ade-437d-81cb-d9f46306d1e0
#check launch policy
launch_policy((stock_state,debris_state))

# ╔═╡ 7dfc61a9-569a-4e97-a292-4c2b15a78e3a
value((stock_state,debris_state))

# ╔═╡ 2f0ff236-0dff-4cd8-bbb4-f1e81ecef308
#=
Setup the physical model

These values are just guesstimates
=#

begin
#Getting loss parameters together.
loss_param = 2e-3;
loss_weights = loss_param*(ones(N_constellations,N_constellations) - LinearAlgebra.I);

#orbital decay rate
decay_param = 0.01;

#debris generation parameters
autocatalysis_param = 0.001;
satellite_loss_debris_rate = 5.0;
launch_debris_rate = 0.05;

#Todo, wrap physical model as a struct with the parameters
bm = BasicModel(
    loss_param
    ,loss_weights
    ,decay_param
    ,autocatalysis_param
    ,satellite_loss_debris_rate
    ,launch_debris_rate
);
end


# ╔═╡ df771ced-e541-4bb2-b6a3-bb562bbaa89c
md"""
### Evaluation loop
"""

# ╔═╡ 6d9a71ea-b9d1-4efd-aa32-9053b96a66e1
#=Actor Critic Loop
This iterates on the actor critic loop, 
=#
with_terminal() do
for iit in 1:12
	w = Flux.params(value)
	θ = Flux.params(launch_policy)
	
	#Get learning parameters
	action = launch_policy((stock_state,debris_state))
	new_stock_state = G(stock_state,debris_state,action,bm)
	new_debris_state = H(stock_state,debris_state,action,bm)
	println(new_debris_state)
	
	#get current value
	current_value = value((stock_state,debris_state))
	new_value = value((new_stock_state,new_debris_state))

	#need to define
	R = F(stock_state,debris_state,action)

	#FIX: R is a vector, so are the values. This needs fixed.
	δ = ((R .- optim.R̄) .+ (new_value .- current_value))[1] #fix
	
	#store values
	optim.R̄ = optim.R̄ + δ*optim.αᴿ

	#check for exit conditions.
	#probably use grad=0
	continue #issue in calculating gradients
	#update the learning traces
	optim.zʷ = optim.λʷ .* optim.zʷ .+ Flux.gradient(value,w)
	optim.zᶿ = optim.λᶿ .* optim.zᶿ .+ Flux.gradient(policy,θ)

	continue
	#update the policies
	w = w .+ δ* optim.αʷ .* optim.zʷ;
	θ = θ .+ δ* optim.αᶿ .* optim.zᶿ;


	println("its working, $iit")
end
end

# ╔═╡ 9941e984-e51b-4e64-8d16-13c8071363b0
#TODO:issue is here, taking gradient. I get to figure this out next.
Zygote.gradient(() -> sum(value(stock_state,debris_state),w))

# ╔═╡ dd9f5f04-31e4-441e-9f5f-3019a30692d6
with_terminal() do
		x = rand(Float32, 10)
		m = Chain(Dense(10, 5, relu), Dense(5, 2), softmax)
		l(x) = Flux.Losses.crossentropy(m(x), [0.5, 0.5])
		grads = gradient(params(m)) do
		    l(x)
		end
		for p in params(m)
		    println(grads[p])
		end
end

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version = "0.2.1"

[[deps.UnsafeAtomicsLLVM]]
deps = ["LLVM", "UnsafeAtomics"]
git-tree-sha1 = "ead6292c02aab389cb29fe64cc9375765ab1e219"
uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
version = "0.1.1"

[[deps.ZipFile]]
deps = ["Libdl", "Printf", "Zlib_jll"]
git-tree-sha1 = "3593e69e469d2111389a9bd06bac1f3d730ac6de"
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
version = "0.9.4"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+0"

[[deps.Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "19c586905e78a26f7e4e97f81716057bd6b1bc54"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.70"

    [deps.Zygote.extensions]
    ZygoteColorsExt = "Colors"
    ZygoteDistancesExt = "Distances"
    ZygoteTrackerExt = "Tracker"

    [deps.Zygote.weakdeps]
    Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
    Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
    Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"

[[deps.ZygoteRules]]
deps = ["ChainRulesCore", "MacroTools"]
git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.5"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.8.0+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.52.0+1"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"
"""

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