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Commit 56f0d07b authored by Antonio Andriella's avatar Antonio Andriella
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main.py
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......@@ -147,18 +147,16 @@ def main():
#################GENERATE SIMULATION################################
# SIMULATION PARAMS
epochs = 10
epochs = 20
scaling_factor = 1
# initialise the agent
bn_model_caregiver_assistance = bnlearn.import_DAG('/home/pal/Documents/Framework/bn_generative_model/bn_agent_model/agent_assistive_model.bif')
bn_model_caregiver_feedback = None#bnlearn.import_DAG('/home/pal/Documents/Framework/bn_generative_model/bn_agent_model/agent_feedback_model.bif')
bn_model_user_action = bnlearn.import_DAG('/home/pal/Documents/Framework/bn_generative_model/bn_persona_model/user_action_model.bif')
bn_model_user_react_time = bnlearn.import_DAG('/home/pal/Documents/Framework/bn_generative_model/bn_persona_model/user_react_time_model.bif')
bn_model_user_action_filename = '/home/pal/Documents/Framework/bn_generative_model/bn_persona_model/persona_model_test.bif'
bn_model_user_action = bnlearn.import_DAG(bn_model_user_action_filename)
#setup by the caregiver
user_pref_assistance = 2
agent_behaviour = "challenge"
# initialise memory, attention and reactivity variables
persona_memory = 0;
persona_attention = 0;
persona_reactivity = 1;
# define state space struct for the irl algorithm
episode_instance = Episode()
......@@ -179,25 +177,22 @@ def main():
terminal_state = [(Game_State.counter.value, i, user_action[j]) for i in range(1, Attempt.counter.value + 1) for j in
range(len(user_action))]
initial_state = (1, 1, 0)
agent_policy = [0 for s in state_space]
#1. RUN THE SIMULATION WITH THE PARAMS SET BY THE CAREGIVER
game_performance_per_episode, react_time_per_episode, agent_assistance_per_episode, agent_feedback_per_episode, episodes_list = \
Sim.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"],
agent_policy=None,
state_space=states_space_list, action_space=action_space_list,
epochs=epochs, task_complexity=5, max_attempt_per_object=4)
Sim.simulation(bn_model_user_action=bn_model_user_action,
var_user_action_target_action=['user_action'],
game_state_bn_name="game_state",
attempt_bn_name="attempt",
agent_assistance_bn_name="agent_assistance",
agent_feedback_bn_name="agent_feedback",
user_pref_assistance=user_pref_assistance,
agent_behaviour=agent_behaviour,
agent_policy=[],
state_space=states_space_list,
action_space=action_space_list,
epochs=epochs, task_complexity=5, max_attempt_per_object=4, alpha_learning=0.1)
#2. GIVEN THE EPISODES ESTIMATE R(S) and PI(S)
......@@ -207,9 +202,9 @@ def main():
if not os.path.exists(full_path):
os.mkdir(full_path)
plot_game_performance_path = "SIM_game_performance_"+"epoch_" + str(epochs) + "_persona_memory_" + str(persona_memory) + "_persona_attention_" + str(persona_attention) + "_persona_reactivity_" + str(persona_reactivity) + ".jpg"
plot_agent_assistance_path = "SIM_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 = "SIM_agent_feedback_"+"epoch_"+str(epochs)+"_persona_memory_"+str(persona_memory)+"_persona_attention_"+str(persona_attention)+"_persona_reactivity_"+str(persona_reactivity)+".jpg"
plot_game_performance_path = "SIM_game_performance_"+"epoch_" + str(epochs) + ".jpg"
plot_agent_assistance_path = "SIM_agent_assistance_"+"epoch_"+str(epochs)+".jpg"
plot_agent_feedback_path = "SIM_agent_feedback_"+"epoch_"+str(epochs)+".jpg"
utils.plot2D_game_performance(full_path +plot_game_performance_path, epochs, scaling_factor, game_performance_per_episode)
utils.plot2D_assistance(full_path + plot_agent_assistance_path, epochs, scaling_factor, agent_assistance_per_episode)
......@@ -227,7 +222,7 @@ def main():
#R(s) and pi(s) generated from the first sim
maxent_R_sim = maxent(world, terminals, episodes_list)
maxent_V_sim, maxent_P_sim = vi.value_iteration(world.p_transition, maxent_R_sim, gamma=0.9, error=1e-3, deterministic=True)
maxent_V_sim, maxent_P_sim = vi.value_iteration(world.p_transition, maxent_R_sim, gamma=0.9, error=1e-3, deterministic=False)
plt.figure(figsize=(12, 4), num="maxent_rew")
sns.heatmap(np.reshape(maxent_R_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
plt.savefig(full_path + "sim_maxent_R.jpg")
......@@ -240,43 +235,31 @@ def main():
#####################################################################################
#3.WE GOT SOME REAL DATA UPDATE THE BELIEF OF THE BN
log_directory = "/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/1/0"
log_directory = "/home/pal/Documents/Framework/bn_generative_model/bn_persona_model/cognitive_game.csv"
if os.path.exists(log_directory):
bn_functions.update_episodes_batch(bn_model_user_action, bn_model_user_react_time,
bn_model_caregiver_assistance,
bn_model_caregiver_feedback, folder_filename=log_directory,
with_caregiver=True)
bn_model_user_action_from_data = Sim.build_model_from_data(csv_filename=log_directory, dag_filename=bn_model_user_action_filename, dag_model=bn_model_user_action)
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, episodes_list = \
Sim.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"],
agent_policy=None,
state_space=states_space_list, action_space=action_space_list,
epochs=epochs, task_complexity=5, max_attempt_per_object=4)
plot_game_performance_path = "REAL_SIM_game_performance_" + "epoch_" + str(epochs) + "_persona_memory_" + str(
persona_memory) + "persona_attention_" + str(persona_attention) + "_persona_reactivity_" + str(
persona_reactivity) + ".jpg"
plot_agent_assistance_path = "REAL_SIM_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 = "REAL_SIM_agent_feedback_" + "epoch_" + str(epochs) + "_persona_memory_" + str(
persona_memory) + "_persona_attention_" + str(persona_attention) + "_persona_reactivity_" + str(
persona_reactivity) + ".jpg"
Sim.simulation(bn_model_user_action=bn_model_user_action,
var_user_action_target_action=['user_action'],
game_state_bn_name="game_state",
attempt_bn_name="attempt",
agent_assistance_bn_name="agent_assistance",
agent_feedback_bn_name="agent_feedback",
user_pref_assistance=user_pref_assistance,
agent_behaviour=agent_behaviour,
agent_policy = maxent_P_sim,
state_space=states_space_list,
action_space=action_space_list,
epochs=epochs, task_complexity=5, max_attempt_per_object=4, alpha_learning=0.1)
plot_game_performance_path = "REAL_SIM_game_performance_" + "epoch_" + str(epochs) + ".jpg"
plot_agent_assistance_path = "REAL_SIM_agent_assistance_" + "epoch_" + str(epochs) + ".jpg"
plot_agent_feedback_path = "REAL_SIM_agent_feedback_" + "epoch_" + str(epochs) + ".jpg"
utils.plot2D_game_performance(full_path + plot_game_performance_path, epochs, scaling_factor, game_performance_per_episode)
utils.plot2D_assistance(full_path + plot_agent_assistance_path, epochs, scaling_factor, agent_assistance_per_episode)
......
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