simplified example to demonstrate how to train an agent to drive with DP

examples/train_sut_to_drive.jl

@@ -2,20 +2,17 @@ using AdversarialDriving
 using POMDPs, POMDPPolicies, POMDPSimulators
 using Distributions
 using GridInterpolations, LocalFunctionApproximation, LocalApproximationValueIteration
-using Serialization
 
-save_folder ="examples/training_hist/"
-Np, Nv = 3, 3
-N_suts = 10
+## setup training params
+Np, Nv = 3,3 #25, 15
+Nsteps = 2#50
 
-# for i=1:N_suts
-i = 1
+## Construct a disturbance model for the adversaries
 da_std = rand(Exponential(0.5))
 goal_toggle_p = 10. ^rand(Uniform(-5., -1.))
 blinker_toggle_p = 10. ^rand(Uniform(-5., -1.))
 v_des = rand(Uniform(15., 30.))
 per_timestep_penalty = rand([0, 1e-4, 1e-3, 5e-3])
-
 disturbances = Sampleable[Normal(0,da_std), Bernoulli(goal_toggle_p), Bernoulli(blinker_toggle_p), Bernoulli(0.), Bernoulli(0.)]
 
 ## construct the MDP
@@ -28,29 +25,6 @@ goals = Float64.(Tint_goals[laneid(adversaries(mdp)[1].get_initial_entity())])
 grid = RectangleGrid(range(0., stop=100.,length=Np), range(0, stop=30., length=Nv), goals, [0.0, 1.0],
                       range(15., stop=100., length=Np), range(0., stop=30., length=Nv), [5.0], [1.0])
 
-interp = LocalGIFunctionApproximator(grid)  # Create the local function approximator using the grid
-solver = LocalApproximationValueIterationSolver(interp, is_mdp_generative = true, n_generative_samples = 10, verbose = true, max_iterations = 25, belres = 1e-6)
+interp = LocalGIFunctionApproximator(grid)
+solver = LocalApproximationValueIterationSolver(interp, is_mdp_generative = true, n_generative_samples = 5, verbose = true, max_iterations = Nsteps)
 policy = solve(solver, mdp)
-
-folder = string(save_folder, "sut_version_", lpad(i, 2, "0"), "/")
-try mkdir(folder) catch end
-println("saving...")
-serialize(string(folder, "mdp"), mdp)
-serialize(string(folder, "policy"), policy)
-
-println("Evaluating the performance...")
-
-h = simulate(HistoryRecorder(max_steps = 150), mdp, policy)
-scenes_to_gif(state_hist(h), mdp.roadway, "out.gif")
-## Evaluate the performance
-tot_suc, Ntrials =0, 10
-for k=1:Ntrials
-    println("k:", k)
-    h = simulate(HistoryRecorder(max_steps = 150), mdp, policy)
-    global tot_suc += any(reward_hist(h) .> 1.)
-end
-tot_suc
-prob_suc =  tot_suc / Ntrials
-println("success prob: ", prob_suc)
-write("performance.txt", string(prob_suc))
-# end