diff --git a/main.py b/main.py
index b619ddbfe1bb9165ccd316b6b6ba953563f55b20..c42ec3899fb1cb5766cb3d8defb169d28b3acd28 100644
--- a/main.py
+++ b/main.py
@@ -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)