From 1eee95336069988e2aac159d026ee63ddaccb626 Mon Sep 17 00:00:00 2001
From: Antonio Andriella <aandriella@iri.upc.edu>
Date: Mon, 21 Sep 2020 22:57:00 +0200
Subject: [PATCH] working version including new function to read real_time
variables and update them into the model
---
main.py | 228 +++++++++++++++++++++++++++++++-------------------------
1 file changed, 126 insertions(+), 102 deletions(-)
diff --git a/main.py b/main.py
index 66133a8..0eb7e52 100644
--- a/main.py
+++ b/main.py
@@ -3,7 +3,7 @@ import os
import bnlearn
import numpy as np
#import classes and modules
-from bn_variables import Robot_Assistance, Robot_Feedback, User_Action, User_React_time, Game_State, Attempt
+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
@@ -47,7 +47,7 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
elif user_action == 0 and attempt_counter < max_attempt_per_object:
attempt_counter += 1
timeout_counter += 1
- # the robot or therapist makes the correct move on the patient's behalf
+ # the agent or therapist makes the correct move on the patient's behalf
else:
attempt_counter = 1
max_attempt_counter += 1
@@ -75,9 +75,9 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
def simulation(bn_model_user_action, var_user_action_target_action, bn_model_user_react_time, var_user_react_time_target_action,
user_memory_name, user_memory_value, user_attention_name, user_attention_value,
user_reactivity_name, user_reactivity_value,
- task_progress_name, game_attempt_name, robot_assistance_name, robot_feedback_name,
- bn_model_robot_assistance, var_robot_assistance_target_action, bn_model_robot_feedback,
- var_robot_feedback_target_action,
+ task_progress_name, game_attempt_name, agent_assistance_name, agent_feedback_name,
+ bn_model_agent_assistance, var_agent_assistance_target_action, bn_model_agent_feedback,
+ var_agent_feedback_target_action,
bn_model_other_user_action, var_other_user_action_target_action,
bn_model_other_user_react_time, var_other_user_target_react_time_action,
other_user_memory_name, other_user_memory_value,
@@ -94,25 +94,25 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
n_timeout_per_episode:
'''
- #TODO: remove robot_assistance_vect and robot_feedback_vect
+ #TODO: remove agent_assistance_vect and agent_feedback_vect
#metrics we need, in order to compute afterwords the belief
attempt_counter_per_action = [[0 for i in range(Attempt.counter.value)] for j in range(User_Action.counter.value)]
game_state_counter_per_action = [[0 for i in range(Game_State.counter.value)] for j in range(User_Action.counter.value)]
- robot_feedback_per_action = [[0 for i in range(Robot_Feedback.counter.value)] for j in range(User_Action.counter.value)]
- robot_assistance_per_action = [[0 for i in range(Robot_Assistance.counter.value)] for j in range(User_Action.counter.value)]
+ agent_feedback_per_action = [[0 for i in range(Agent_Feedback.counter.value)] for j in range(User_Action.counter.value)]
+ agent_assistance_per_action = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(User_Action.counter.value)]
attempt_counter_per_react_time = [[0 for i in range(Attempt.counter.value)] for j in range(User_React_time.counter.value)]
game_state_counter_per_react_time = [[0 for i in range(Game_State.counter.value)] for j in range(User_React_time.counter.value)]
- robot_feedback_per_react_time = [[0 for i in range(Robot_Feedback.counter.value)] for j in range(User_React_time.counter.value)]
- robot_assistance_per_react_time = [[0 for i in range(Robot_Assistance.counter.value)] for j in range(User_React_time.counter.value)]
+ agent_feedback_per_react_time = [[0 for i in range(Agent_Feedback.counter.value)] for j in range(User_React_time.counter.value)]
+ agent_assistance_per_react_time = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(User_React_time.counter.value)]
- game_state_counter_per_robot_assistance = [[0 for i in range(Game_State.counter.value)] for j in range(Robot_Assistance.counter.value)]
- attempt_counter_per_robot_assistance = [[0 for i in range(Attempt.counter.value)] for j in range(Robot_Assistance.counter.value)]
+ game_state_counter_per_agent_assistance = [[0 for i in range(Game_State.counter.value)] for j in range(Agent_Assistance.counter.value)]
+ attempt_counter_per_agent_assistance = [[0 for i in range(Attempt.counter.value)] for j in range(Agent_Assistance.counter.value)]
- game_state_counter_per_robot_feedback = [[0 for i in range(Game_State.counter.value)] for j in range(Robot_Feedback.counter.value)]
- attempt_counter_per_robot_feedback = [[0 for i in range(Attempt.counter.value)] for j in range(Robot_Feedback.counter.value)]
+ game_state_counter_per_agent_feedback = [[0 for i in range(Game_State.counter.value)] for j in range(Agent_Feedback.counter.value)]
+ attempt_counter_per_agent_feedback = [[0 for i in range(Attempt.counter.value)] for j in range(Agent_Feedback.counter.value)]
#output variables:
@@ -121,8 +121,8 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
n_timeout_per_episode = [0]*epochs
n_max_attempt_per_episode = [0]*epochs
game_performance_episode = [0]*epochs
- n_assistance_lev_per_episode = [[0 for i in range(Robot_Assistance.counter.value)] for j in range(epochs)]
- n_feedback_per_episode = [[0 for i in range(Robot_Feedback.counter.value)] for j in range(epochs)]
+ n_assistance_lev_per_episode = [[0 for i in range(Agent_Assistance.counter.value)] for j in range(epochs)]
+ n_feedback_per_episode = [[0 for i in range(Agent_Feedback.counter.value)] for j in range(epochs)]
n_react_time_per_episode = [[0 for i in range(User_React_time.counter.value)] for j in range(epochs)]
@@ -144,19 +144,19 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
#The following variables are used to update the BN at the end of the episode
user_action_dynamic_variables = {'attempt': attempt_counter_per_action,
'game_state': game_state_counter_per_action,
- 'robot_assistance': robot_assistance_per_action,
- 'robot_feedback': robot_feedback_per_action}
+ 'agent_assistance': agent_assistance_per_action,
+ 'agent_feedback': agent_feedback_per_action}
user_react_time_dynamic_variables = {'attempt': attempt_counter_per_react_time,
'game_state': game_state_counter_per_react_time,
- 'robot_assistance': robot_assistance_per_react_time,
- 'robot_feedback': robot_feedback_per_react_time}
+ 'agent_assistance': agent_assistance_per_react_time,
+ 'agent_feedback': agent_feedback_per_react_time}
- robot_assistance_dynamic_variables = {'attempt': attempt_counter_per_robot_assistance,
- 'game_state': game_state_counter_per_robot_assistance}
+ agent_assistance_dynamic_variables = {'attempt': attempt_counter_per_agent_assistance,
+ 'game_state': game_state_counter_per_agent_assistance}
- robot_feedback_dynamic_variables = {'attempt': attempt_counter_per_robot_feedback,
- 'game_state': game_state_counter_per_robot_feedback}
+ agent_feedback_dynamic_variables = {'attempt': attempt_counter_per_agent_feedback,
+ 'game_state': game_state_counter_per_agent_feedback}
#data structure to memorise the sequence of states (state, action, next_state)
episode = []
@@ -168,30 +168,30 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
current_state = (game_state_counter, attempt_counter, selected_user_action)
##################QUERY FOR THE ROBOT ASSISTANCE AND FEEDBACK##################
- vars_robot_evidence = {
+ vars_agent_evidence = {
user_reactivity_name: user_reactivity_value,
user_memory_name: user_memory_value,
task_progress_name: game_state_counter,
game_attempt_name: attempt_counter-1,
}
- query_robot_assistance_prob = bn_functions.infer_prob_from_state(bn_model_robot_assistance,
- infer_variable=var_robot_assistance_target_action,
- evidence_variables=vars_robot_evidence)
+ query_agent_assistance_prob = bn_functions.infer_prob_from_state(bn_model_agent_assistance,
+ infer_variable=var_agent_assistance_target_action,
+ evidence_variables=vars_agent_evidence)
- query_robot_feedback_prob = bn_functions.infer_prob_from_state(bn_model_robot_feedback,
- infer_variable=var_robot_feedback_target_action,
- evidence_variables=vars_robot_evidence)
+ query_agent_feedback_prob = bn_functions.infer_prob_from_state(bn_model_agent_feedback,
+ infer_variable=var_agent_feedback_target_action,
+ evidence_variables=vars_agent_evidence)
- selected_robot_assistance_action = bn_functions.get_stochastic_action(query_robot_assistance_prob.values)
- selected_robot_feedback_action = bn_functions.get_stochastic_action(query_robot_feedback_prob.values)
+ selected_agent_assistance_action = bn_functions.get_stochastic_action(query_agent_assistance_prob.values)
+ selected_agent_feedback_action = bn_functions.get_stochastic_action(query_agent_feedback_prob.values)
#counters for plots
- n_assistance_lev_per_episode[e][selected_robot_assistance_action] += 1
- n_feedback_per_episode[e][selected_robot_feedback_action] += 1
- current_robot_action = (selected_robot_assistance_action, selected_robot_feedback_action)
+ n_assistance_lev_per_episode[e][selected_agent_assistance_action] += 1
+ n_feedback_per_episode[e][selected_agent_feedback_action] += 1
+ current_agent_action = (selected_agent_assistance_action, selected_agent_feedback_action)
- print("robot_assistance {}, attempt {}, game {}, robot_feedback {}".format(selected_robot_assistance_action, attempt_counter, game_state_counter, selected_robot_feedback_action))
+ print("agent_assistance {}, attempt {}, game {}, agent_feedback {}".format(selected_agent_assistance_action, attempt_counter, game_state_counter, selected_agent_feedback_action))
##########################QUERY FOR THE USER ACTION AND REACT TIME#####################################
@@ -203,8 +203,8 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
other_user_memory_name:other_user_memory_value,
task_progress_name:game_state_counter,
game_attempt_name:attempt_counter-1,
- robot_assistance_name:selected_robot_assistance_action,
- robot_feedback_name:selected_robot_feedback_action
+ agent_assistance_name:selected_agent_assistance_action,
+ agent_feedback_name:selected_agent_feedback_action
}
query_user_action_prob = bn_functions.infer_prob_from_state(bn_model_other_user_action,
infer_variable=var_other_user_action_target_action,
@@ -221,8 +221,8 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
user_memory_name: user_memory_value,
task_progress_name: game_state_counter,
game_attempt_name: attempt_counter-1,
- robot_assistance_name: selected_robot_assistance_action,
- robot_feedback_name: selected_robot_feedback_action
+ agent_assistance_name: selected_agent_assistance_action,
+ agent_feedback_name: selected_agent_feedback_action
}
query_user_action_prob = bn_functions.infer_prob_from_state(bn_model_user_action,
infer_variable=var_user_action_target_action,
@@ -239,21 +239,21 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
n_react_time_per_episode[e][selected_user_react_time] += 1
#updates counters for user action
- robot_assistance_per_action[selected_user_action][selected_robot_assistance_action] += 1
+ agent_assistance_per_action[selected_user_action][selected_agent_assistance_action] += 1
attempt_counter_per_action[selected_user_action][attempt_counter-1] += 1
game_state_counter_per_action[selected_user_action][game_state_counter] += 1
- robot_feedback_per_action[selected_user_action][selected_robot_feedback_action] += 1
+ agent_feedback_per_action[selected_user_action][selected_agent_feedback_action] += 1
#update counter for user react time
- robot_assistance_per_react_time[selected_user_react_time][selected_robot_assistance_action] += 1
+ agent_assistance_per_react_time[selected_user_react_time][selected_agent_assistance_action] += 1
attempt_counter_per_react_time[selected_user_react_time][attempt_counter-1] += 1
game_state_counter_per_react_time[selected_user_react_time][game_state_counter] += 1
- robot_feedback_per_react_time[selected_user_react_time][selected_robot_feedback_action] += 1
- #update counter for robot feedback
- game_state_counter_per_robot_feedback[selected_robot_feedback_action][game_state_counter] += 1
- attempt_counter_per_robot_feedback[selected_robot_feedback_action][attempt_counter-1] += 1
- #update counter for robot assistance
- game_state_counter_per_robot_assistance[selected_robot_assistance_action][game_state_counter] += 1
- attempt_counter_per_robot_assistance[selected_robot_assistance_action][attempt_counter-1] += 1
+ agent_feedback_per_react_time[selected_user_react_time][selected_agent_feedback_action] += 1
+ #update counter for agent feedback
+ game_state_counter_per_agent_feedback[selected_agent_feedback_action][game_state_counter] += 1
+ attempt_counter_per_agent_feedback[selected_agent_feedback_action][attempt_counter-1] += 1
+ #update counter for agent assistance
+ game_state_counter_per_agent_assistance[selected_agent_assistance_action][game_state_counter] += 1
+ attempt_counter_per_agent_assistance[selected_agent_assistance_action][attempt_counter-1] += 1
# updates counters for simulation
# remap user_action index
@@ -276,7 +276,7 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
# store the (state, action, next_state)
episode.append((ep.point_to_index(current_state, state_space),
- ep.point_to_index(current_robot_action, action_space),
+ ep.point_to_index(current_agent_action, action_space),
ep.point_to_index(next_state, state_space)))
print("current_state ", current_state, " next_state ", next_state)
@@ -284,10 +284,10 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
print("game_state_counter {}, iter_counter {}, correct_counter {}, wrong_counter {}, "
"timeout_counter {}, max_attempt {}".format(game_state_counter, iter_counter, correct_move_counter,
wrong_move_counter, timeout_counter, max_attempt_counter))
- # print("robot_assistance_per_action {}".format(robot_assistance_per_action))
+ # print("agent_assistance_per_action {}".format(agent_assistance_per_action))
# print("attempt_counter_per_action {}".format(attempt_counter_per_action))
# print("game_state_counter_per_action {}".format(game_state_counter_per_action))
- # print("robot_feedback_per_action {}".format(robot_feedback_per_action))
+ # print("agent_feedback_per_action {}".format(agent_feedback_per_action))
# print("iter {}, correct {}, wrong {}, timeout {}".format(iter_counter, correct_move_counter, wrong_move_counter, timeout_counter))
#save episode
@@ -296,14 +296,14 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
#update user models
bn_model_user_action = bn_functions.update_cpds_tables(bn_model_user_action, user_action_dynamic_variables)
bn_model_user_react_time = bn_functions.update_cpds_tables(bn_model_user_react_time, user_react_time_dynamic_variables)
- #update robot models
- bn_model_robot_assistance = bn_functions.update_cpds_tables(bn_model_robot_assistance, robot_assistance_dynamic_variables)
- bn_model_robot_feedback = bn_functions.update_cpds_tables(bn_model_robot_feedback, robot_feedback_dynamic_variables)
+ #update agent models
+ bn_model_agent_assistance = bn_functions.update_cpds_tables(bn_model_agent_assistance, agent_assistance_dynamic_variables)
+ bn_model_agent_feedback = bn_functions.update_cpds_tables(bn_model_agent_feedback, agent_feedback_dynamic_variables)
#reset counter
- robot_assistance_per_action = [[0 for i in range(Robot_Assistance.counter.value)] for j in
+ agent_assistance_per_action = [[0 for i in range(Agent_Assistance.counter.value)] for j in
range(User_Action.counter.value)]
- robot_feedback_per_action = [[0 for i in range(Robot_Feedback.counter.value)] for j in
+ agent_feedback_per_action = [[0 for i in range(Agent_Feedback.counter.value)] for j in
range(User_Action.counter.value)]
game_state_counter_per_action = [[0 for i in range(Game_State.counter.value)] for j in
range(User_Action.counter.value)]
@@ -314,20 +314,20 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
range(User_React_time.counter.value)]
game_state_counter_per_react_time = [[0 for i in range(Game_State.counter.value)] for j in
range(User_React_time.counter.value)]
- robot_feedback_per_react_time = [[0 for i in range(Robot_Feedback.counter.value)] for j in
+ agent_feedback_per_react_time = [[0 for i in range(Agent_Feedback.counter.value)] for j in
range(User_React_time.counter.value)]
- robot_assistance_per_react_time = [[0 for i in range(Robot_Assistance.counter.value)] for j in
+ agent_assistance_per_react_time = [[0 for i in range(Agent_Assistance.counter.value)] for j in
range(User_React_time.counter.value)]
- game_state_counter_per_robot_assistance = [[0 for i in range(Game_State.counter.value)] for j in
- range(Robot_Assistance.counter.value)]
- attempt_counter_per_robot_assistance = [[0 for i in range(Attempt.counter.value)] for j in
- range(Robot_Assistance.counter.value)]
+ game_state_counter_per_agent_assistance = [[0 for i in range(Game_State.counter.value)] for j in
+ range(Agent_Assistance.counter.value)]
+ attempt_counter_per_agent_assistance = [[0 for i in range(Attempt.counter.value)] for j in
+ range(Agent_Assistance.counter.value)]
- game_state_counter_per_robot_feedback = [[0 for i in range(Game_State.counter.value)] for j in
- range(Robot_Feedback.counter.value)]
- attempt_counter_per_robot_feedback = [[0 for i in range(Attempt.counter.value)] for j in
- range(Robot_Feedback.counter.value)]
+ game_state_counter_per_agent_feedback = [[0 for i in range(Game_State.counter.value)] for j in
+ range(Agent_Feedback.counter.value)]
+ attempt_counter_per_agent_feedback = [[0 for i in range(Attempt.counter.value)] for j in
+ range(Agent_Feedback.counter.value)]
#for plots
n_correct_per_episode[e] = correct_move_counter
@@ -351,12 +351,11 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
#############################################################################
#SIMULATION PARAMS
-
epochs = 10
-#initialise the robot
-bn_model_robot_assistance = bnlearn.import_DAG('bn_robot_model/robot_assistive_model.bif')
-bn_model_robot_feedback = bnlearn.import_DAG('bn_robot_model/robot_feedback_model.bif')
+#initialise the agent
+bn_model_caregiver_assistance = bnlearn.import_DAG('bn_agent_model/agent_assistive_model.bif')
+bn_model_caregiver_feedback = bnlearn.import_DAG('bn_agent_model/agent_feedback_model.bif')
bn_model_user_action = bnlearn.import_DAG('bn_persona_model/user_action_model.bif')
bn_model_user_react_time = bnlearn.import_DAG('bn_persona_model/user_react_time_model.bif')
bn_model_other_user_action = None#bnlearn.import_DAG('bn_persona_model/other_user_action_model.bif')
@@ -374,43 +373,68 @@ game_state = [i for i in range(0, Game_State.counter.value+1)]
user_action = [i for i in range(-1, User_Action.counter.value-1)]
state_space = (game_state, attempt, user_action)
states_space_list = list(itertools.product(*state_space))
-robot_assistance_action = [i for i in range(Robot_Assistance.counter.value)]
-robot_feedback_action = [i for i in range(Robot_Feedback.counter.value)]
-action_space = (robot_assistance_action, robot_feedback_action)
+agent_assistance_action = [i for i in range(Agent_Assistance.counter.value)]
+agent_feedback_action = [i for i in range(Agent_Feedback.counter.value)]
+action_space = (agent_assistance_action, agent_feedback_action)
action_space_list = list(itertools.product(*action_space))
-game_performance_per_episode, react_time_per_episode, robot_assistance_per_episode, robot_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",
- robot_assistance_name="robot_assistance", robot_feedback_name="robot_feedback",
- bn_model_robot_assistance=bn_model_robot_assistance, var_robot_assistance_target_action=["robot_assistance"],
- bn_model_robot_feedback=bn_model_robot_feedback, var_robot_feedback_target_action=["robot_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)
+##############BEFORE RUNNING THE SIMULATION UPDATE THE BELIEF IF YOU HAVE DATA####################
+bn_belief_user_action_file = "/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/0/bn_belief_user_action.pkl"
+bn_belief_user_react_time_file = "/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/0/bn_belief_user_react_time.pkl"
+bn_belief_caregiver_assistance_file = "/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/0/bn_belief_caregiver_assistive_action.pkl"
+bn_belief_caregiver_feedback_file = "/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/0/bn_belief_caregiver_feedback_action.pkl"
+if bn_belief_user_action_file != None and bn_belief_user_react_time_file!= None and \
+ bn_belief_caregiver_assistance_file!=None and bn_belief_caregiver_feedback_file!=None:
+ bn_belief_user_action = utils.read_user_statistics_from_pickle(bn_belief_user_action_file)
+ bn_belief_user_react_time = utils.read_user_statistics_from_pickle(bn_belief_user_react_time_file)
+ bn_belief_caregiver_assistance = utils.read_user_statistics_from_pickle(bn_belief_caregiver_assistance_file)
+ bn_belief_caregiver_feedback = utils.read_user_statistics_from_pickle(bn_belief_caregiver_feedback_file)
+ bn_model_user_action = bn_functions.update_cpds_tables(bn_model=bn_model_user_action, variables_tables=bn_belief_user_action)
+ bn_model_user_react_time = bn_functions.update_cpds_tables(bn_model=bn_model_user_react_time, variables_tables=bn_belief_user_react_time)
+ bn_model_caregiver_assistance = bn_functions.update_cpds_tables(bn_model=bn_model_caregiver_assistance, variables_tables=bn_belief_caregiver_assistance)
+ bn_model_caregiver_feedback = bn_functions.update_cpds_tables(bn_model=bn_model_caregiver_feedback, variables_tables=bn_belief_caregiver_feedback)
+
+ 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)
+
+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?")
+
+
plot_game_performance_path = ""
-plot_robot_assistance_path = ""
+plot_agent_assistance_path = ""
episodes_path = "episodes.npy"
if bn_model_other_user_action != None:
plot_game_performance_path = "game_performance_"+"_epoch_"+str(epochs)+"_real_user_memory_"+str(real_user_memory)+"_real_user_attention_"+str(real_user_attention)+"_real_user_reactivity_"+str(real_user_reactivity)+".jpg"
- plot_robot_assistance_path = "robot_assistance_"+"epoch_"+str(epochs)+"_real_user_memory_"+str(real_user_memory)+"_real_user_attention_"+str(real_user_attention)+"_real_user_reactivity_"+str(real_user_reactivity)+".jpg"
- plot_robot_feedback_path = "robot_feedback_"+"epoch_"+str(epochs)+"_real_user_memory_"+str(real_user_memory)+"_real_user_attention_"+str(real_user_attention)+"_real_user_reactivity_"+str(real_user_reactivity)+".jpg"
+ plot_agent_assistance_path = "agent_assistance_"+"epoch_"+str(epochs)+"_real_user_memory_"+str(real_user_memory)+"_real_user_attention_"+str(real_user_attention)+"_real_user_reactivity_"+str(real_user_reactivity)+".jpg"
+ plot_agent_feedback_path = "agent_feedback_"+"epoch_"+str(epochs)+"_real_user_memory_"+str(real_user_memory)+"_real_user_attention_"+str(real_user_attention)+"_real_user_reactivity_"+str(real_user_reactivity)+".jpg"
else:
plot_game_performance_path = "game_performance_"+"epoch_" + str(epochs) + "_persona_memory_" + str(persona_memory) + "_persona_attention_" + str(persona_attention) + "_persona_reactivity_" + str(persona_reactivity) + ".jpg"
- plot_robot_assistance_path = "robot_assistance_"+"epoch_"+str(epochs)+"_persona_memory_"+str(persona_memory)+"_persona_attention_"+str(persona_attention)+"_persona_reactivity_"+str(persona_reactivity)+".jpg"
- plot_robot_feedback_path = "robot_feedback_"+"epoch_"+str(epochs)+"_persona_memory_"+str(persona_memory)+"_persona_attention_"+str(persona_attention)+"_persona_reactivity_"+str(persona_reactivity)+".jpg"
+ plot_agent_assistance_path = "agent_assistance_"+"epoch_"+str(epochs)+"_persona_memory_"+str(persona_memory)+"_persona_attention_"+str(persona_attention)+"_persona_reactivity_"+str(persona_reactivity)+".jpg"
+ plot_agent_feedback_path = "agent_feedback_"+"epoch_"+str(epochs)+"_persona_memory_"+str(persona_memory)+"_persona_attention_"+str(persona_attention)+"_persona_reactivity_"+str(persona_reactivity)+".jpg"
dir_name = input("Please insert the name of the directory:")
full_path = os.getcwd()+"/results/"+dir_name+"/"
@@ -430,8 +454,8 @@ with open(full_path+episodes_path, "ab") as f:
utils.plot2D_game_performance(full_path+plot_game_performance_path, epochs, game_performance_per_episode)
-utils.plot2D_assistance(full_path+plot_robot_assistance_path, epochs, robot_assistance_per_episode)
-utils.plot2D_feedback(full_path+plot_robot_feedback_path, epochs, robot_feedback_per_episode)
+utils.plot2D_assistance(full_path+plot_agent_assistance_path, epochs, agent_assistance_per_episode)
+utils.plot2D_feedback(full_path+plot_agent_feedback_path, epochs, agent_feedback_per_episode)
@@ -443,7 +467,7 @@ the episodes will be used to generate the trans probabilities and as input to th
# - include reaction time as output
# - average mistakes, average timeout, average assistance, average_react_time
# - include real time episodes into the simulation:
-# - counters for robot_assistance, robot_feedback, attempt, game_state, attention and reactivity
+# - counters for agent_assistance, agent_feedback, attempt, game_state, attention and reactivity
# - using the function update probability to generate the new user model and use it as input to the simulator
--
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