Save simulation data and plt.clf() to plot more than figures at the time

main.py

@@ -282,6 +282,7 @@ def main():
     learned_policy_filename = args.agent_patient_interaction_folder+"/"+str(user_id)+"/"+str(with_feedback)+"/"+str(session+1)+"/"+args.output_policy_filename
     learned_reward_filename = args.agent_patient_interaction_folder+"/"+str(user_id)+"/"+str(with_feedback)+"/"+str(session+1)+"/"+args.output_reward_filename
     learned_value_f_filename = args.agent_patient_interaction_folder+"/"+str(user_id)+"/"+str(with_feedback)+"/"+str(session+1)+"/"+args.output_value_filename
+
     therapist_patient_interaction_folder = args.therapist_patient_interaction_folder
     agent_patient_interaction_folder = args.agent_patient_interaction_folder
     scaling_factor = 1
@@ -308,7 +309,7 @@ def main():
     action_space_list = action_space
     terminal_state = [(Game_State.counter.value, i, user_action[j]) for i in range(1, Attempt.counter.value + 1) for j in
                       range(len(user_action))]
-    initial_state = (1, 1, 0)
+    initial_state = (1, 1, 1)
 
 
     #output folders
@@ -350,7 +351,6 @@ def main():
     bn_model_agent_behaviour_from_data_and_therapist = None
 
 
-
     if os.path.exists(output_folder_data_path):
         bn_model_user_action_from_data_and_therapist = Sim.build_model_from_data(
             csv_filename=output_folder_data_path + "/summary_bn_variables_from_data.csv", dag_filename=bn_user_model_filename,
@@ -385,6 +385,14 @@ def main():
     utils.plot2D_game_performance(plot_game_performance_path, epochs, scaling_factor, game_performance_per_episode)
     utils.plot2D_assistance(plot_agent_assistance_path, epochs, scaling_factor, agent_assistance_per_episode)
 
+    sim_patient_performance_filename = "sim_patient_performance.pkl"
+    sim_agent_assistance_filename = "sim_agent_assistance.pkl"
+
+    with open(output_folder_data_path+"/"+sim_agent_assistance_filename, 'wb') as f:
+        pickle.dump(game_performance_per_episode, f, protocol=2)
+    with open(output_folder_data_path + "/" + sim_patient_performance_filename, 'wb') as f:
+        pickle.dump(agent_assistance_per_episode, f, protocol=2)
+
     # add episodes from different policies
     # for e in range(len(episodes)):
     #     episodes_from_different_policies.append(Episode(episodes[e]._t))
@@ -402,7 +410,7 @@ def main():
 
     # R(s) and pi(s) generated from the first sim
     maxent_R = maxent(world=cognitive_game_world, terminal=terminals, trajectories=episodes)
-    maxent_V, maxent_P = vi.value_iteration(cognitive_game_world.p_transition, maxent_R, gamma=0.99, error=1e-2,
+    maxent_V, maxent_P = vi.value_iteration(cognitive_game_world.p_transition, maxent_R, gamma=0.9, error=1e-4,
                                                               deterministic=False)
     print(maxent_P)
     with open(learned_policy_filename, 'wb') as f:
@@ -425,18 +433,22 @@ def main():
     #             else:
     #                 maxent_P_real_sim[state_index][action_index] = 0.02
     #         maxent_P_real_sim[state_index] = list(map(lambda x:x/sum(maxent_P_real_sim[state_index]), maxent_P_real_sim[state_index]))
-
+    plt.clf()
     sns.heatmap(np.reshape(maxent_R, (4, 12)), cmap="Spectral", annot=True, cbar=False)
     plt.savefig(output_folder_data_path + "/maxent_R.jpg")
-    plt.show()
+    plt.clf()
     sns.heatmap(np.reshape(maxent_V, (4, 12)), cmap="Spectral", annot=True, cbar=False)
     plt.savefig(output_folder_data_path + "/maxent_V.jpg")
-    plt.show()
+    plt.clf()
     maxent_P_det = list(map(lambda x: np.argmax(x), maxent_P))
     sns.heatmap(np.reshape(maxent_P_det, (4, 12)), cmap="Spectral", annot=True, cbar=False)
     plt.savefig(output_folder_data_path + "/maxent_P.jpg")
-    plt.show()
+    plt.clf()
 
+    f = open(output_folder_data_path+"/"+sim_agent_assistance_filename,'rb')
+    mydic = pickle.load(f)
+    f.close()
+    print(mydic)
 
 
 if __name__ == '__main__':