read from real time data from caregiver, fix bug on absorbing states

main.py

@@ -6,7 +6,7 @@ import numpy as np
 from bn_variables import Agent_Assistance, Agent_Feedback, User_Action, User_React_time, Game_State, Attempt
 import bn_functions
 import utils
-import episode as ep
+from episode import Episode
 
 
 def compute_next_state(user_action, task_progress_counter, attempt_counter, correct_move_counter,
@@ -32,38 +32,43 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
         max_attempt_counter
     '''
 
+    if task_progress_counter >= 0 and task_progress_counter < 2:
+        game_state_counter = 0
+    elif task_progress_counter >= 2 and task_progress_counter < 4:
+        game_state_counter = 1
+    elif task_progress_counter >= 4 and task_progress_counter < 5:
+        game_state_counter = 2
+    else:
+        game_state_counter = 3
 
     # if then else are necessary to classify the task game state into beg, mid, end
 
-    if user_action == 1:
-      attempt_counter = 1
-      correct_move_counter += 1
-      task_progress_counter += 1
+    if user_action == 1 and game_state_counter<3:
+        attempt_counter = 1
+        correct_move_counter += 1
+        task_progress_counter += 1
     # if the user made a wrong move and still did not reach the maximum number of attempts
-    elif user_action == -1 and attempt_counter < max_attempt_per_object:
-      attempt_counter += 1
-      wrong_move_counter += 1
+    elif user_action == -1 and attempt_counter < max_attempt_per_object and game_state_counter<3:
+        attempt_counter += 1
+        wrong_move_counter += 1
     # if the user did not move any token and still did not reach the maximum number of attempts
-    elif user_action == 0 and attempt_counter < max_attempt_per_object:
-      attempt_counter += 1
-      timeout_counter += 1
+    elif user_action == 0 and attempt_counter < max_attempt_per_object and game_state_counter<3:
+        attempt_counter += 1
+        timeout_counter += 1
     # the agent or therapist makes the correct move on the patient's behalf
-    else:
-      attempt_counter = 1
-      max_attempt_counter += 1
-      task_progress_counter +=1
+    elif attempt_counter>=max_attempt_per_object and game_state_counter<3:
+        attempt_counter = 1
+        max_attempt_counter += 1
+        task_progress_counter +=1
 
+    if game_state_counter==3:
+        attempt_counter = 1
+        task_progress_counter +=1
+        print("Reach the end of the episode")
 
     # TODO call the function to compute the state of the game (beg, mid, end)
 
-    if correct_move_counter >= 0 and correct_move_counter <= 2:
-      game_state_counter = 0
-    elif correct_move_counter > 2 and correct_move_counter <= 4:
-      game_state_counter = 1
-    elif correct_move_counter>4 and correct_move_counter<=5:
-      game_state_counter = 2
-    else:
-      game_state_counter = 3
+
 
 
     next_state = (game_state_counter, attempt_counter, user_action)
@@ -128,7 +133,7 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
 
     #data structure to memorise a sequence of episode
     episodes = []
-
+    ep = Episode()
 
     for e in range(epochs):
         '''Simulation framework'''
@@ -275,9 +280,9 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
                                                                         max_attempt_per_object)
 
             # store the (state, action, next_state)
-            episode.append((ep.point_to_index(current_state, state_space),
-                            ep.point_to_index(current_agent_action, action_space),
-                            ep.point_to_index(next_state, state_space)))
+            episode.append((ep.state_from_point_to_index(state_space, current_state,),
+                            ep.state_from_point_to_index(action_space, current_agent_action),
+                            ep.state_from_point_to_index(state_space, next_state)))
 
             print("current_state ", current_state, " next_state ", next_state)
         ####################################END of EPISODE#######################################
@@ -351,7 +356,7 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
 #############################################################################
 
 #SIMULATION PARAMS
-epochs = 10
+epochs = 100
 
 #initialise the agent
 bn_model_caregiver_assistance = bnlearn.import_DAG('bn_agent_model/agent_assistive_model.bif')
@@ -418,7 +423,29 @@ if bn_belief_user_action_file != None and bn_belief_user_react_time_file!= None
 
 else:
     assert("You're not using the user information")
-    question = raw_input("Are you sure you don't want to load user's belief information?")
+    question = input("Are you sure you don't want to load user's belief information?")
+    game_performance_per_episode, react_time_per_episode, agent_assistance_per_episode, agent_feedback_per_episode, generated_episodes = \
+        simulation(bn_model_user_action=bn_model_user_action, var_user_action_target_action=['user_action'],
+                   bn_model_user_react_time=bn_model_user_react_time,
+                   var_user_react_time_target_action=['user_react_time'],
+                   user_memory_name="memory", user_memory_value=persona_memory,
+                   user_attention_name="attention", user_attention_value=persona_attention,
+                   user_reactivity_name="reactivity", user_reactivity_value=persona_reactivity,
+                   task_progress_name="game_state", game_attempt_name="attempt",
+                   agent_assistance_name="agent_assistance", agent_feedback_name="agent_feedback",
+                   bn_model_agent_assistance=bn_model_caregiver_assistance,
+                   var_agent_assistance_target_action=["agent_assistance"],
+                   bn_model_agent_feedback=bn_model_caregiver_feedback,
+                   var_agent_feedback_target_action=["agent_feedback"],
+                   bn_model_other_user_action=bn_model_other_user_action,
+                   var_other_user_action_target_action=['user_action'],
+                   bn_model_other_user_react_time=bn_model_other_user_react_time,
+                   var_other_user_target_react_time_action=["user_react_time"], other_user_memory_name="memory",
+                   other_user_memory_value=other_user_memory, other_user_attention_name="attention",
+                   other_user_attention_value=other_user_attention, other_user_reactivity_name="reactivity",
+                   other_user_reactivity_value=other_user_reactivity,
+                   state_space=states_space_list, action_space=action_space_list,
+                   epochs=epochs, task_complexity=5, max_attempt_per_object=4)