more thread-safe repeatability

examples/geothermal_example.jl

@@ -7,7 +7,7 @@ using CSV
 using Random
 using DataStructures
 using Plots
-default(framestyle = :box,  color_palette=:seaborn_deep6, fontfamily="Computer Modern")
+default(framestyle = :box,  color_palette=:seaborn_deep6, fontfamily="Computer Modern", margin=5mm)
 
 # Define random seeds
 fix_solve_and_eval_seed = true # Whether the seed is set before each policy gen and evaluation. Seed is the eval index + test set. It is threadsafe. 
@@ -140,14 +140,14 @@ option11_pol(pomdp) = FixedPolicy([Symbol("Option 10")])
 option13_pol(pomdp) = FixedPolicy([Symbol("Option 11")])
 all_policy_geo(pomdp) = EnsureParticleCount(FixedPolicy(obs_actions, BestCurrentOption(pomdp)), BestCurrentOption(pomdp), min_particles)
 all_policy_econ(pomdp) = EnsureParticleCount(FixedPolicy(reverse(obs_actions), BestCurrentOption(pomdp)), BestCurrentOption(pomdp), min_particles)
-random_policy_10(pomdp) = EnsureParticleCount(RandPolicy(;pomdp, prob_terminal=0.1), BestCurrentOption(pomdp), min_particles)
-random_policy_4(pomdp) = EnsureParticleCount(RandPolicy(;pomdp, prob_terminal=0.25), BestCurrentOption(pomdp), min_particles)
-random_policy_2(pomdp) = EnsureParticleCount(RandPolicy(;pomdp, prob_terminal=0.5), BestCurrentOption(pomdp), min_particles)
-voi_policy(pomdp) = EnsureParticleCount(OneStepGreedyPolicy(;pomdp), BestCurrentOption(pomdp), min_particles)
-sarsop_policy(pomdp) = EnsureParticleCount(solve(SARSOPSolver(), pomdp), BestCurrentOption(pomdp), min_particles)
+random_policy_0_1(pomdp) = EnsureParticleCount(RandPolicy(;pomdp, prob_terminal=0.1), BestCurrentOption(pomdp), min_particles)
+random_policy_0_25(pomdp) = EnsureParticleCount(RandPolicy(;pomdp, prob_terminal=0.25), BestCurrentOption(pomdp), min_particles)
+random_policy_0_5(pomdp) = EnsureParticleCount(RandPolicy(;pomdp, prob_terminal=0.5), BestCurrentOption(pomdp), min_particles)
+# voi_policy(pomdp) = EnsureParticleCount(OneStepGreedyPolicy(;pomdp), BestCurrentOption(pomdp), min_particles)
+sarsop_policy(pomdp) = EnsureParticleCount(solve(SARSOPSolver(max_time=10.0), pomdp), BestCurrentOption(pomdp), min_particles)
 
 # combine policies into a list
-policies = [option7_pol, option11_pol, option13_pol, all_policy_geo, all_policy_econ, random_policy_10, random_policy_4, random_policy_2, sarsop_policy] # voi_policy
+policies = [option7_pol, option11_pol, option13_pol, all_policy_geo, all_policy_econ, random_policy_0_1, random_policy_0_25, random_policy_0_5, sarsop_policy] # voi_policy
 policy_names = ["Option 7", "Option 11", "Option 13", "All Data Policy (Geo First)", "All Data Policy (Econ First)", "Random Policy (Pstop=0.1)", "Random Policy (Pstop=0.25)", "Random Policy (Pstop=0.5)", "SARSOP Policy"] # "VOI Policy"
 
 # Evaluate the policies on the test set 
@@ -229,7 +229,17 @@ elseif split_by == :both
 end
 
 zip_fracs = [(g,e) for (g, e) in zip(geo_fracs, econ_fracs)]
-pomdps_per_geo, test_sets_per_geo = create_pomdps_with_different_training_fractions(zip_fracs, scenario_csvs, geo_params, econ_params, obs_actions, Nbins; rng_seed=pomdp_gen_seed, discount=discount_factor, split_by)
+pomdps_per_geo, test_sets_per_geo = create_pomdps_with_different_training_fractions(
+    zip_fracs, 
+    scenario_csvs, 
+    geo_params, 
+    econ_params,
+    obs_actions, 
+    Nbins; 
+    rng_seed=pomdp_gen_seed, 
+    discount=discount_factor, 
+    split_by)
+
 
 # Solve the policies and evaluate the results #<---- Uncomment the below lines to solve and eval the policies
 results = Dict()

src/pomdp.jl

@@ -296,16 +296,16 @@ function create_pomdps(scenario_csvs, geo_params, econ_params, obs_actions, Nbin
     p = Progress(nfolds, 1, "Creating POMDPs...")
     Threads.@threads for i in 1:nfolds
         # Create a specific rng for each thread
-        thread_rng = MersenneTwister(i)
+        Random.seed!(rng_seed+i)
 
         # Generate the train set by combining all of the folds except the ith one
         train = train_sets[i]
 
         # Discretize the observations
-        discrete_obs = get_discrete_observations(train, obs_actions, Nbins; rng=thread_rng)
+        discrete_obs = get_discrete_observations(train, obs_actions, Nbins;)
 
         # Create categorical observation distributions
-        obs_dists = create_observation_distributions(train, obs_actions, discrete_obs, Nbins .* Nsamples_per_bin; rng=thread_rng)
+        obs_dists = create_observation_distributions(train, obs_actions, discrete_obs, Nbins .* Nsamples_per_bin;)
 
         # Make the POMDP and return the val and test sets
         pomdps[i] = InfoGatheringPOMDP(train, obs_actions, keys(scenario_csvs), discrete_obs, obs_dists, (o) -> nearest_neighbor_mapping(o, discrete_obs), discount)