working version

main.py

@@ -29,12 +29,12 @@ import argparse
 from episode import Episode
 from cognitive_game_env import CognitiveGame
 from environment import Environment
-import maxent as M
-import plot as P
-import solver as S
-import optimizer as O
-import img_utils as I
-import value_iteration as vi
+import src.maxent as M
+import src.plot as P
+import src.solver as S
+import src.optimizer as O
+import src.img_utils as I
+import src.value_iteration as vi
 
 import simulation as Sim
 import bn_functions as bn_functions
@@ -237,8 +237,9 @@ def compute_agent_policy(training_set_filename, state_space, action_space, episo
             action_index = action_point
             agent_policy_counter[state_index][action_index] += 1
             row_t_0 = row['user_action']
+    min_val = np.finfo(float).eps
     for s in range(len(state_space)):
-        agent_policy_prob[s] = list(map(lambda x:x/(sum(agent_policy_counter[s])+0.001), agent_policy_counter[s]))
+        agent_policy_prob[s] = list(map(lambda x:x/(sum(agent_policy_counter[s])+min_val), agent_policy_counter[s]))
 
     return agent_policy_prob
 
@@ -248,8 +249,8 @@ def main():
     parser = argparse.ArgumentParser()
     parser.add_argument('--bn_model_folder', '--bn_model_folder', type=str,help="folder in which all the user and the agent models are stored ",
                         default="/home/pal/Documents/Framework/GenerativeMutualShapingRL/BN_Models")
-    parser.add_argument('--bn_agent_model_filename', '--bn_agent_model', type=str,help="file path of the agent bn model",
-                        default="/home/pal/Documents/Framework/bn_generative_model/bn_agent_model/agent_test.bif")
+    # parser.add_argument('--bn_agent_model_filename', '--bn_agent_model', type=str,help="file path of the agent bn model",
+    #                     default="/home/pal/Documents/Framework/bn_generative_model/bn_agent_model/agent_test.bif")
     parser.add_argument('--epoch', '--epoch', type=int,help="number of epochs in the simulation", default=200)
     parser.add_argument('--run', '--run', type=int, help="number of runs in the simulation", default=50)
     parser.add_argument('--output_policy_filename', '--p', type=str,help="output policy from the simulation",
@@ -259,9 +260,9 @@ def main():
     parser.add_argument('--output_value_filename', '--v', type=str, help="output value function from the simulation",
                         default="value_function.pkl")
     parser.add_argument('--therapist_patient_interaction_folder', '--tpi_path', type=str,help="therapist-patient interaction folder",
-                        default="/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log")
+                        default="/home/pal/Documents/Framework/GenerativeMutualShapingRL/therapist-patient-interaction")
     parser.add_argument('--agent_patient_interaction_folder', '--api_path', type=str,help="agent-patient interaction folder",
-                        default="/home/pal/carf_ws/src/carf/robot_in_the_loop/log")
+                        default="/home/pal/carf_ws/src/carf/robot-patient-interaction")
     parser.add_argument('--user_id', '--id', type=int,help="user id", required=True)
     parser.add_argument('--with_feedback', '--f', type=eval, choices=[True, False], help="offering sociable", required=True)
     parser.add_argument('--session', '--s', type=int, help="session of the agent-human interaction", required=True)
@@ -278,9 +279,9 @@ def main():
     # initialise the agent
     bn_user_model_filename = args.bn_model_folder  +"/"+str(user_id)+"/"+str(with_feedback)+"/user_model.bif"
     bn_agent_model_filename = args.bn_model_folder+"/"+str(user_id)+"/"+str(with_feedback)+"/agent_model.bif"
-    learned_policy_filename = args.output_policy_filename
-    learned_reward_filename = args.output_reward_filename
-    learned_value_f_filename = args.output_value_filename
+    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
@@ -312,6 +313,7 @@ def main():
 
     #output folders
     output_folder_data_path = os.getcwd() + "/results/" + str(user_id) +"/"+str(with_feedback)+"/"+str(session)
+
     if not os.path.exists(os.getcwd() + "/results"+"/"+str(user_id)):
         os.mkdir(os.getcwd() + "/results"+"/"+str(user_id))
     if not os.path.exists(os.getcwd() + "/results"+"/"+str(user_id) +"/"+str(with_feedback)):
@@ -319,13 +321,19 @@ def main():
     if not os.path.exists(output_folder_data_path):
         os.mkdir(output_folder_data_path)
 
+    if not os.path.exists(args.agent_patient_interaction_folder+"/"+str(user_id)):
+        os.mkdir(args.agent_patient_interaction_folder+"/"+str(user_id))
+    if not os.path.exists(args.agent_patient_interaction_folder+"/"+str(user_id)+"/"+str(with_feedback)):
+        os.mkdir(args.agent_patient_interaction_folder+"/"+str(user_id)+"/"+str(with_feedback))
+    if not os.path.exists(args.agent_patient_interaction_folder + "/" + str(user_id) + "/" + str(with_feedback) + "/" + str(session + 1)):
+        os.mkdir(args.agent_patient_interaction_folder + "/" + str(user_id) + "/" + str(with_feedback) + "/" + str(session + 1))
 
-#1. CREATE INITIAL USER COGNITIVE MODEL FROM DATA
+    #1. CREATE INITIAL USER COGNITIVE MODEL FROM DATA
     df_from_data, episode_length = merge_user_log(tpi_folder_pathname=therapist_patient_interaction_folder,
                                                   file_output=output_folder_data_path+"/summary_bn_variables_from_data.csv",
                                                   user_id=user_id,
                                                   with_feedback=with_feedback,
-                                                  rpi_folder_pathname=None,#agent_patient_interaction_folder,
+                                                  rpi_folder_pathname=agent_patient_interaction_folder,
                                                 column_to_remove=None)
 
     #2. CREATE POLICY FROM DATA
@@ -397,11 +405,11 @@ def main():
     maxent_V, maxent_P = vi.value_iteration(cognitive_game_world.p_transition, maxent_R, gamma=0.99, error=1e-2,
                                                               deterministic=False)
     print(maxent_P)
-    with open(output_folder_data_path+"/"+learned_policy_filename, 'wb') as f:
+    with open(learned_policy_filename, 'wb') as f:
         pickle.dump(maxent_P, f, protocol=2)
-    with open(output_folder_data_path+"/"+learned_reward_filename, 'wb') as f:
+    with open(learned_reward_filename, 'wb') as f:
         pickle.dump(maxent_R, f, protocol=2)
-    with open(output_folder_data_path+"/"+learned_value_f_filename, 'wb') as f:
+    with open(learned_value_f_filename, 'wb') as f:
         pickle.dump(maxent_V, f, protocol=2)
 
     # if n>0: