diff --git a/main.py b/main.py
index dcbf08465c08966617aa326ac77981471f76f392..ab68aac2423f96d884620d98db1b10244fca5bc7 100644
--- a/main.py
+++ b/main.py
@@ -15,7 +15,7 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
'''
The function computes given the current state and action of the user, the next state
Args:
- user_action: -1 wrong, 0 timeout, 1 correct
+ user_action: -1a wrong, 0 timeout, 1a correct
game_state_counter: beg, mid, end
correct_move_counter:
attempt_counter:
@@ -182,25 +182,26 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
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_agent_feedback_prob = bn_functions.infer_prob_from_state(bn_model_agent_feedback,
+ if bn_model_agent_feedback != None:
+ 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_agent_feedback_action = bn_functions.get_stochastic_action(query_agent_feedback_prob.values)
+ else:
+ selected_agent_feedback_action = 0
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)
+ n_feedback_per_episode[e][selected_agent_feedback_action] += 1
#counters for plots
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("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#####################################
- #compare the real user with the estimated Persona and returns a user action (0, 1, 2)
+ #compare the real user with the estimated Persona and returns a user action (0, 1a, 2)
if bn_model_other_user_action!=None and bn_model_user_react_time!=None:
#return the user action in this state based on the user profile
vars_other_user_evidence = {other_user_attention_name:other_user_attention_value,
@@ -289,11 +290,6 @@ 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("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("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
episodes.append(episode)
@@ -373,7 +369,7 @@ other_user_memory = 2; other_user_attention = 2; other_user_reactivity = 2;
#define state space struct for the irl algorithm
attempt = [i for i in range(1, Attempt.counter.value+1)]
-#+1 (3,_,_) absorbing state
+#+1a (3,_,_) absorbing state
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)
@@ -384,12 +380,13 @@ action_space = (agent_assistance_action, agent_feedback_action)
action_space_list = list(itertools.product(*action_space))
##############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:
+log_directory = ""
+if os.path.exists(log_directory):
+ bn_belief_user_action_file = log_directory+"/bn_belief_user_action.pkl"
+ bn_belief_user_react_time_file = log_directory+"/bn_belief_user_react_time.pkl"
+ bn_belief_caregiver_assistance_file = log_directory+"/bn_belief_caregiver_assistive_action.pkl"
+ bn_belief_caregiver_feedback_file = log_directory+"/bn_belief_caregiver_feedback_action.pkl"
+
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)
@@ -399,32 +396,11 @@ if bn_belief_user_action_file != None and bn_belief_user_react_time_file!= None
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 = 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 = \
+
+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'],
@@ -435,8 +411,7 @@ else:
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_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,