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https://gitlab.rlp.net/mobitar/ReCo.jl.git
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Added reward discount analysis
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commit
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4 changed files with 65 additions and 11 deletions
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@ -160,7 +160,7 @@ function plot_radial_distributions(;
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max_g = maximum(maximum.(gs))
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ax = Axis(
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fig[1:2, 1:2];
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fig[1, 1];
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xticks=0:(2 * particle_radius):floor(Int64, max_lower_radius),
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yticks=0:ceil(Int64, max_g),
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xlabel=L"r / d",
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49
analysis/reward_discount_analysis.jl
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49
analysis/reward_discount_analysis.jl
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@ -0,0 +1,49 @@
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using CairoMakie
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using LaTeXStrings: @L_str
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using ReCo: ReCo
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includet("../src/Visualization/common_CairoMakie.jl")
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function run_reward_discount_analysis()
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γs = 0.0:0.2:1.0
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n_γs = length(γs)
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γ_rewards = Vector{Vector{Float64}}(undef, n_γs)
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Threads.@threads for γ_ind in 1:n_γs
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γ = γs[γ_ind]
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env_helper = ReCo.run_rl(;
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EnvType=ReCo.OriginEnv,
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n_episodes=500,
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episode_duration=8.0,
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n_particles=200,
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update_actions_at=0.08,
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ϵ_stable=0.00001,
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process_dir="reward_discount_analysis/$γ_ind",
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reward_discount=γ,
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)
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rewards = env_helper.shared.hook.rewards
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γ_rewards[γ_ind] = rewards
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end
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init_cairomakie!()
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fig = gen_figure()
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ax = Axis(fig[1, 1]; xlabel="Episode", ylabel="Reward")
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rewards_plots = []
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for (rewards_ind, rewards) in enumerate(γ_rewards)
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rewards_plot = lines!(ax, 1:length(rewards), rewards)
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push!(rewards_plots, rewards_plot)
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end
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Legend(fig[1, 2], rewards_plots, [L"\gamma = %$γ" for γ in γs])
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set_gaps!(fig)
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save_fig("reward_discount_analysis.pdf", fig)
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return nothing
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end
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@ -26,7 +26,9 @@ include("States.jl")
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include("Hooks.jl")
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include("Reward.jl")
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function gen_agent(n_states::Int64, n_actions::Int64, ϵ_stable::Float64)
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function gen_agent(
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n_states::Int64, n_actions::Int64, ϵ_stable::Float64, reward_discount::Float64
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)
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# TODO: Optimize warmup and decay
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warmup_steps = 500_000
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decay_steps = 5_000_000
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@ -36,7 +38,7 @@ function gen_agent(n_states::Int64, n_actions::Int64, ϵ_stable::Float64)
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approximator=TabularQApproximator(;
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n_state=n_states, n_action=n_actions, opt=Flux.InvDecay(1.0)
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),
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γ=0.95, # Reward discount
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γ=reward_discount,
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),
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explorer=EpsilonGreedyExplorer(;
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kind=:linear,
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@ -67,6 +69,7 @@ function run_rl(;
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skin_to_interaction_radius_ratio::Float64=ReCo.DEFAULT_SKIN_TO_INTERACTION_R_RATIO,
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packing_ratio::Float64=0.15,
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show_progress::Bool=true,
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reward_discount::Float64=1.0,
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) where {E<:Env}
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@assert 0.0 <= elliptical_a_b_ratio <= 1.0
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@assert n_episodes > 0
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@ -89,7 +92,7 @@ function run_rl(;
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env_args = (skin_radius=sim_consts.skin_radius, half_box_len=sim_consts.half_box_len)
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env = EnvType(; args=env_args)
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agent = gen_agent(env.shared.n_states, env.shared.n_actions, ϵ_stable)
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agent = gen_agent(env.shared.n_states, env.shared.n_actions, ϵ_stable, reward_discount)
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n_steps_before_actions_update = round(Int64, update_actions_at / sim_consts.δt)
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@ -1,7 +1,9 @@
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using DataFrames
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using DataFrames: DataFrames
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using PrettyTables: pretty_table
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function latex_table(dataframe, filename::String; path="exports/$filename")
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function latex_table(
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dataframe::DataFrames.DataFrame, filename::String; path::String="exports/$filename"
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)
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open(path, "w") do f
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pretty_table(f, dataframe; backend=:latex, nosubheader=true, alignment=:c)
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end
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@ -9,8 +11,8 @@ function latex_table(dataframe, filename::String; path="exports/$filename")
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return nothing
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end
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function latex_rl_table(env_helper, filename)
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table = env_helper.shared.agent.policy.learner.approximator.table
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function latex_rl_table(env_helper, filename::String)
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table = copy(env_helper.shared.agent.policy.learner.approximator.table)
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for col in 1:size(table)[2]
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table[:, col] ./= sum(table[:, col])
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@ -31,12 +33,12 @@ function latex_rl_table(env_helper, filename)
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for i in action_spaces_labels[1]
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for j in action_spaces_labels[2]
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push!(actions, i * "; " * j)
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push!(actions, i * " ; " * j)
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end
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end
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df = DataFrame(table, states)
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insertcols!(df, 1, :Actions => actions)
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df = DataFrames.DataFrame(table, states)
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DataFrames.insertcols!(df, 1, :Actions => actions)
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latex_table(df, filename)
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