Added reward discount analysis

analysis/radial_distribution_function/radial_distribution_function.jl

@@ -160,7 +160,7 @@ function plot_radial_distributions(;
     max_g = maximum(maximum.(gs))
 
     ax = Axis(
-        fig[1:2, 1:2];
+        fig[1, 1];
         xticks=0:(2 * particle_radius):floor(Int64, max_lower_radius),
         yticks=0:ceil(Int64, max_g),
         xlabel=L"r / d",

analysis/reward_discount_analysis.jl

@@ -0,0 +1,49 @@
+using CairoMakie
+using LaTeXStrings: @L_str
+
+using ReCo: ReCo
+
+includet("../src/Visualization/common_CairoMakie.jl")
+
+function run_reward_discount_analysis()
+    γs = 0.0:0.2:1.0
+    n_γs = length(γs)
+    γ_rewards = Vector{Vector{Float64}}(undef, n_γs)
+
+    Threads.@threads for γ_ind in 1:n_γs
+        γ = γs[γ_ind]
+        env_helper = ReCo.run_rl(;
+            EnvType=ReCo.OriginEnv,
+            n_episodes=500,
+            episode_duration=8.0,
+            n_particles=200,
+            update_actions_at=0.08,
+            ϵ_stable=0.00001,
+            process_dir="reward_discount_analysis/$γ_ind",
+            reward_discount=γ,
+        )
+
+        rewards = env_helper.shared.hook.rewards
+        γ_rewards[γ_ind] = rewards
+    end
+
+    init_cairomakie!()
+
+    fig = gen_figure()
+
+    ax = Axis(fig[1, 1]; xlabel="Episode", ylabel="Reward")
+
+    rewards_plots = []
+    for (rewards_ind, rewards) in enumerate(γ_rewards)
+        rewards_plot = lines!(ax, 1:length(rewards), rewards)
+        push!(rewards_plots, rewards_plot)
+    end
+
+    Legend(fig[1, 2], rewards_plots, [L"\gamma = %$γ" for γ in γs])
+
+    set_gaps!(fig)
+
+    save_fig("reward_discount_analysis.pdf", fig)
+
+    return nothing
+end