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Antonio Andriella
BN_GenerativeModel
Commits
6ec87b1d
Commit
6ec87b1d
authored
4 years ago
by
Antonio Andriella
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improving the simulator
parent
16c58bfc
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main.py
+19
-44
19 additions, 44 deletions
main.py
with
19 additions
and
44 deletions
main.py
+
19
−
44
View file @
6ec87b1d
...
@@ -15,7 +15,7 @@ def compute_next_state(user_action, task_progress_counter, attempt_counter, corr
...
@@ -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
The function computes given the current state and action of the user, the next state
Args:
Args:
user_action: -1 wrong, 0 timeout, 1 correct
user_action: -1
a
wrong, 0 timeout, 1
a
correct
game_state_counter: beg, mid, end
game_state_counter: beg, mid, end
correct_move_counter:
correct_move_counter:
attempt_counter:
attempt_counter:
...
@@ -182,25 +182,26 @@ def simulation(bn_model_user_action, var_user_action_target_action, bn_model_use
...
@@ -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
,
query_agent_assistance_prob
=
bn_functions
.
infer_prob_from_state
(
bn_model_agent_assistance
,
infer_variable
=
var_agent_assistance_target_action
,
infer_variable
=
var_agent_assistance_target_action
,
evidence_variables
=
vars_agent_evidence
)
evidence_variables
=
vars_agent_evidence
)
if
bn_model_agent_feedback
!=
None
:
query_agent_feedback_prob
=
bn_functions
.
infer_prob_from_state
(
bn_model_agent_feedback
,
query_agent_feedback_prob
=
bn_functions
.
infer_prob_from_state
(
bn_model_agent_feedback
,
infer_variable
=
var_agent_feedback_target_action
,
infer_variable
=
var_agent_feedback_target_action
,
evidence_variables
=
vars_agent_evidence
)
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_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
#counters for plots
n_assistance_lev_per_episode
[
e
][
selected_agent_assistance_action
]
+=
1
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
)
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
))
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#####################################
##########################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, 1
a
, 2)
if
bn_model_other_user_action
!=
None
and
bn_model_user_react_time
!=
None
:
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
#return the user action in this state based on the user profile
vars_other_user_evidence
=
{
other_user_attention_name
:
other_user_attention_value
,
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
...
@@ -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 {},
"
print
(
"
game_state_counter {}, iter_counter {}, correct_counter {}, wrong_counter {},
"
"
timeout_counter {}, max_attempt {}
"
.
format
(
game_state_counter
,
iter_counter
,
correct_move_counter
,
"
timeout_counter {}, max_attempt {}
"
.
format
(
game_state_counter
,
iter_counter
,
correct_move_counter
,
wrong_move_counter
,
timeout_counter
,
max_attempt_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
#save episode
episodes
.
append
(
episode
)
episodes
.
append
(
episode
)
...
@@ -373,7 +369,7 @@ other_user_memory = 2; other_user_attention = 2; other_user_reactivity = 2;
...
@@ -373,7 +369,7 @@ other_user_memory = 2; other_user_attention = 2; other_user_reactivity = 2;
#define state space struct for the irl algorithm
#define state space struct for the irl algorithm
attempt
=
[
i
for
i
in
range
(
1
,
Attempt
.
counter
.
value
+
1
)]
attempt
=
[
i
for
i
in
range
(
1
,
Attempt
.
counter
.
value
+
1
)]
#+1 (3,_,_) absorbing state
#+1
a
(3,_,_) absorbing state
game_state
=
[
i
for
i
in
range
(
0
,
Game_State
.
counter
.
value
+
1
)]
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
)]
user_action
=
[
i
for
i
in
range
(
-
1
,
User_Action
.
counter
.
value
-
1
)]
state_space
=
(
game_state
,
attempt
,
user_action
)
state_space
=
(
game_state
,
attempt
,
user_action
)
...
@@ -384,12 +380,13 @@ action_space = (agent_assistance_action, agent_feedback_action)
...
@@ -384,12 +380,13 @@ action_space = (agent_assistance_action, agent_feedback_action)
action_space_list
=
list
(
itertools
.
product
(
*
action_space
))
action_space_list
=
list
(
itertools
.
product
(
*
action_space
))
##############BEFORE RUNNING THE SIMULATION UPDATE THE BELIEF IF YOU HAVE DATA####################
##############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
"
log_directory
=
""
bn_belief_user_react_time_file
=
"
/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/0/bn_belief_user_react_time.pkl
"
if
os
.
path
.
exists
(
log_directory
):
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_user_action_file
=
log_directory
+
"
/bn_belief_user_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
"
bn_belief_user_react_time_file
=
log_directory
+
"
/bn_belief_user_react_time.pkl
"
if
bn_belief_user_action_file
!=
None
and
bn_belief_user_react_time_file
!=
None
and
\
bn_belief_caregiver_assistance_file
=
log_directory
+
"
/bn_belief_caregiver_assistive_action.pkl
"
bn_belief_caregiver_assistance_file
!=
None
and
bn_belief_caregiver_feedback_file
!=
None
:
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_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_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_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
...
@@ -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_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
)
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
:
else
:
assert
(
"
You
'
re not using the user information
"
)
assert
(
"
You
'
re not using the user information
"
)
question
=
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
=
\
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
'
],
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
,
bn_model_user_react_time
=
bn_model_user_react_time
,
var_user_react_time_target_action
=
[
'
user_react_time
'
],
var_user_react_time_target_action
=
[
'
user_react_time
'
],
...
@@ -435,8 +411,7 @@ else:
...
@@ -435,8 +411,7 @@ else:
agent_assistance_name
=
"
agent_assistance
"
,
agent_feedback_name
=
"
agent_feedback
"
,
agent_assistance_name
=
"
agent_assistance
"
,
agent_feedback_name
=
"
agent_feedback
"
,
bn_model_agent_assistance
=
bn_model_caregiver_assistance
,
bn_model_agent_assistance
=
bn_model_caregiver_assistance
,
var_agent_assistance_target_action
=
[
"
agent_assistance
"
],
var_agent_assistance_target_action
=
[
"
agent_assistance
"
],
bn_model_agent_feedback
=
bn_model_caregiver_feedback
,
bn_model_agent_feedback
=
bn_model_caregiver_feedback
,
var_agent_feedback_target_action
=
[
"
agent_feedback
"
],
var_agent_feedback_target_action
=
[
"
agent_feedback
"
],
bn_model_other_user_action
=
bn_model_other_user_action
,
bn_model_other_user_action
=
bn_model_other_user_action
,
var_other_user_action_target_action
=
[
'
user_action
'
],
var_other_user_action_target_action
=
[
'
user_action
'
],
bn_model_other_user_react_time
=
bn_model_other_user_react_time
,
bn_model_other_user_react_time
=
bn_model_other_user_react_time
,
...
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