baseline cfg

cfg/airsim_baseline.cfg

@@ -12,7 +12,7 @@ actor_lr = 5e-5
 n_train_episodes = 400
 beta_coeff = 0.993
 test_interval = 40
-n_test_episodes = 3
+n_test_episodes = 20
 
 # architecture parameters
 k = 2

results/airsim_trained2.csv

@@ -0,0 +1,6 @@
+k, reward, std
+1, -6.1264006465977925, 2.6261114461138306
+2, -3.4652359381486413, 1.3084831858508834
+3, -2.9150179750214456, 1.6347459314744373
+4, -2.591131502530944, 0.5262619174971477
+

results/n.csv

@@ -1,23 +1,19 @@
 k, n_agents, reward
-1, 25, -419.36150946154413, 228.81464681638593
 1, 50, -432.9951122485818, 249.61795362199805
 1, 75, -319.83185308018284, 130.14053520184876
 1, 100, -229.15979292372407, 122.16289677324487
 1, 125, -217.1340116775613, 95.3461704293501
 1, 150, -172.14025454122398, 51.635444445613196
-2, 25, -175.80376085839316, 190.7653062240825
 2, 50, -89.97712855183387, 108.03483725875923
 2, 75, -55.540402898312344, 11.146641164414202
 2, 100, -50.51020781200708, 12.566522373659023
 2, 125, -46.76860348867619, 12.555701772579592
 2, 150, -37.0106466777022, 3.232041202865438
-3, 25, -99.70804057695489, 115.87676249230482
 3, 50, -73.35025660496456, 109.79197822945807
 3, 75, -44.86283894212626, 4.923115098403242
 3, 100, -40.95792156471694, 4.1560902247655385
 3, 125, -41.15206481867972, 15.84355642218285
 3, 150, -34.25862769738046, 2.3905789668612885
-4, 25, -129.51668905023533, 221.61694939867266
 4, 50, -44.96090491863062, 8.438123529368031
 4, 75, -41.707591033111626, 4.70898675648138
 4, 100, -38.293215563303235, 3.1199301223969256

results/n_baseline.csv

@@ -1,11 +1,9 @@
 centralized, n_agents, reward
-True, 25, -28.363746468002706, 6.456637188954312
 True, 50, -21.189087350417914, 4.914202107841018
 True, 75, -17.578174984474465, 3.725134289661792
 True, 100, -14.899697200560103, 1.8157149806086754
 True, 125, -14.497312413551816, 1.857963983379794
 True, 150, -13.436401447590432, 1.508457092067479
-False, 25, -4674.500102991744, 1087.290091768198
 False, 50, -5414.270146056273, 1222.4711829412147
 False, 75, -6172.847920185132, 791.1410941872234
 False, 100, -6535.8762397044575, 554.0006754735136

results/plot_bargraph.py

@@ -29,8 +29,8 @@ def main():
     mean_cost_decent = [6.840906470373157]
     std_cost_decent =  [1.6711923712693055]
 
-    mean_costs_airsim, std_costs_airsim = get_dict(['airsim_trained.csv'], k_ind)
-    mean_costs_stoch, std_costs_stoch = get_dict(['stoch_transfer_to_airsim.csv'], k_ind)
+    mean_costs_airsim, std_costs_airsim = get_dict(['airsim_trained2.csv'], k_ind)
+    mean_costs_stoch, std_costs_stoch = get_dict(['stoch_transfer_to_airsim2.csv'], k_ind)
     ylabel = 'Cost'
 
     # plot
@@ -47,7 +47,7 @@ def main():
     p4 = ax.bar(-1, mean_cost_decent, width=width*3,  yerr=std_cost_decent)
 
 
-    ax.legend((p1[0], p2[0], p3[0], p4[0]), ('Trained: AirSim', 'Trained: Point-Masses', 'Centralized', 'Decentralized'))
+    ax.legend((p1[0], p2[0], p3[0], p4[0]), ('Trained: AirSim', 'Trained: Point-Masses', 'Global', 'Local'))
     plt.title('Testing in AirSim')
     # plt.ylim(top=max_val, bottom=0)
     plt.xlabel('K')

results/plot_results.py

@@ -10,11 +10,11 @@
 matplotlib.rc('font', **font)
 
 _CENTRALIZED = 'Global'
-_DECENTRALIZED = 'Local.'
+_DECENTRALIZED = 'Local'
 
 def main():
 
-    fig_fname = 'rad'
+    fig_fname = 'n'
 
     if fig_fname == 'transfer_grid_n':
         fnames = ['transfer_grid_n.csv']

results/stoch_transfer_to_airsim2.csv

@@ -0,0 +1,6 @@
+k, reward, std
+1, -5.371718155747085, 2.107049489042026
+2, -3.8907236770070335, 1.9490529683049205
+3, -3.5587343557409077, 2.3253053480811885
+4, -4.410319276747424, 2.4066994410746116
+

test_one_traj.py

@@ -47,7 +47,7 @@ def test(args, actor_path, k):
 
     learner.load_model(actor_path, device)
 
-    n_steps = 500
+    n_steps = 400
 
     steps = np.zeros((n_steps,))
     min_dists_mean = np.zeros((n_steps,))
@@ -80,35 +80,37 @@ def test(args, actor_path, k):
 
         if step % 300 == 0:
             env.render()
-            plt.savefig(save_dir + 'traj' + str(step) + '.eps', format='eps')
+            plt.savefig(save_dir + 'leader_traj' + str(step) + '.eps', format='eps')
 
-    plt.ioff()
-
-    y = min_dists_mean
-    y_min = min_dists_mean - min_dists_std
-    y_max = min_dists_mean + min_dists_std
-
-    fig = plt.figure()
-    plt.plot(steps, y, 'b-')
-    plt.fill_between(steps, y_min, y_max, color='lightblue')
-    plt.xlabel('Step')
-    plt.ylabel('Min. Distances')
-    plt.tight_layout()
-    plt.savefig(save_dir + 'min_dist.eps', format='eps')
     plt.show()
 
-    y = vel_diffs_mean
-    y_min = vel_diffs_mean - vel_diffs_std
-    y_max = vel_diffs_mean + vel_diffs_std
-
-    fig = plt.figure()
-    plt.plot(steps, y, 'r-')
-    plt.fill_between(steps, y_min, y_max, color='orange')
-    plt.xlabel('Step')
-    plt.ylabel('Velocity Diff.')
-    plt.tight_layout()
-    plt.savefig(save_dir + 'vel_diff.eps', format='eps')
-    plt.show()
+    plt.ioff()
+    #
+    # y = min_dists_mean
+    # y_min = min_dists_mean - min_dists_std
+    # y_max = min_dists_mean + min_dists_std
+    #
+    # fig = plt.figure()
+    # plt.plot(steps, y, 'b-')
+    # plt.fill_between(steps, y_min, y_max, color='lightblue')
+    # plt.xlabel('Step')
+    # plt.ylabel('Min. Distances')
+    # plt.tight_layout()
+    # plt.savefig(save_dir + 'min_dist.eps', format='eps')
+    # plt.show()
+    #
+    # y = vel_diffs_mean
+    # y_min = vel_diffs_mean - vel_diffs_std
+    # y_max = vel_diffs_mean + vel_diffs_std
+    #
+    # fig = plt.figure()
+    # plt.plot(steps, y, 'r-')
+    # plt.fill_between(steps, y_min, y_max, color='orange')
+    # plt.xlabel('Step')
+    # plt.ylabel('Velocity Diff.')
+    # plt.tight_layout()
+    # plt.savefig(save_dir + 'vel_diff.eps', format='eps')
+    # plt.show()
 
     env.close()
 
@@ -120,13 +122,21 @@ def main():
     # actor_path = 'models/ddpg_actor_FlockingRelative-v0_k3'
     # actor_path = 'models/ddpg_actor_FlockingStochastic-v0_stoch2'
 
-    k = 3
-    actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k)
-    fname = 'cfg/dagger.cfg'
+    # k = 3
+    # actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k)
+    # fname = 'cfg/dagger.cfg'
 
     # actor_path = 'models/ddpg_actor_FlockingStochastic-v0_transfer_stoch' + str(k)
     # fname = 'cfg/dagger_stoch.cfg'
 
+
+    k = 3
+    actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k)
+    fname = 'cfg/dagger_leader.cfg'
+
+
+
+
     config_file = path.join(path.dirname(__file__), fname)
     config = configparser.ConfigParser()
     config.read(config_file)

test_one_traj2.py

@@ -112,8 +112,10 @@ def test(args, actor_path, k):
     plt.plot(steps, y, 'b-', label='GNN')
     plt.fill_between(steps, y_min, y_max, color='lightblue')
 
-    plt.plot(steps, y2, 'r-', label='Decentralized')
+    plt.plot(steps, y2, 'r-', label='Local')
     plt.fill_between(steps, y_min2, y_max2, color='orange')
+
+    plt.axhline(y=1, linestyle='--', color='k', label='Comm. Radius')
     plt.legend()
 
     plt.xlabel('Step')
@@ -134,7 +136,7 @@ def test(args, actor_path, k):
     plt.plot(steps, y, 'b-', label='GNN')
     plt.fill_between(steps, y_min, y_max, color='lightblue')
 
-    plt.plot(steps, y2, 'r-', label='Decentralized')
+    plt.plot(steps, y2, 'r-', label='Local')
     plt.fill_between(steps, y_min2, y_max2, color='orange')
     plt.legend()
     plt.xlabel('Step')