diff --git a/main.py b/main.py
index ce21653926ed8f9af187d90f7c85c37c65d6a2d2..b619ddbfe1bb9165ccd316b6b6ba953563f55b20 100644
--- a/main.py
+++ b/main.py
@@ -155,7 +155,7 @@ def main():
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')
- # initialise memory, attention and reactivity varibles
+ # initialise memory, attention and reactivity variables
persona_memory = 0;
persona_attention = 0;
persona_reactivity = 1;
@@ -180,28 +180,9 @@ def main():
range(len(user_action))]
initial_state = (1, 1, 0)
- # attempt = [i for i in range(1, Attempt.counter.value + 1)]
- # # +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)
- # states_space_list = list(itertools.product(*state_space))
- # 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))
+ #1. RUN THE SIMULATION WITH THE PARAMS SET BY THE CAREGIVER
- ##############BEFORE RUNNING THE SIMULATION UPDATE THE BELIEF IF YOU HAVE DATA####################
- log_directory = "/home/pal/carf_ws/src/carf/caregiver_in_the_loop/log/1/0"
- 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)
- 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,
@@ -218,6 +199,7 @@ def main():
state_space=states_space_list, action_space=action_space_list,
epochs=epochs, task_complexity=5, max_attempt_per_object=4)
+ #2. GIVEN THE EPISODES ESTIMATE R(S) and PI(S)
format = "%a%b%d-%H:%M:%S %Y"
today_id = datetime.datetime.today()
@@ -225,9 +207,9 @@ def main():
if not os.path.exists(full_path):
os.mkdir(full_path)
- plot_game_performance_path = "BEFORE_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 = "BEFORE_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 = "BEFORE_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) + "_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"
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)
@@ -239,41 +221,35 @@ def main():
state_tuple_indexed = [states_space_list.index(tuple(s)) for s in (states_space_list)]
+ #Dirty way to represent the state space in a graphical way
states_space_list_string = [[str(states_space_list[j*12+i]) for i in range(12)] for j in range(3)]
-
-
build_2dtable(states_space_list_string, 3, 12)
- # exp_V, exp_P = vi.value_iteration(world.p_transition, reward, gamma=0.9, error=1e-3, deterministic=True)
- # plt.figure(figsize=(12, 4), num="state_space")
- # sns.heatmap(np.reshape(state_tuple_indexed, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- # plt.savefig(full_path+"state_space.jpg")
- # #PLOTS EXPERT
- # plt.figure(figsize=(12, 4), num="exp_rew")
- # sns.heatmap(np.reshape(reward, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- # plt.savefig(full_path+"exp_rew.jpg")
- # plt.figure(figsize=(12, 4), num="exp_V")
- # sns.heatmap(np.reshape(exp_V, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- # plt.savefig(full_path+"exp_V.jpg")
- # plt.figure(figsize=(12, 4), num="exp_P")
- # sns.heatmap(np.reshape(exp_P, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- # plt.savefig(full_path+"exp_P.jpg")
-
- maxent_R = maxent(world, terminals, episodes_list)
- maxent_V, maxent_P = vi.value_iteration(world.p_transition, maxent_R, gamma=0.9, error=1e-3, deterministic=True)
+ #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)
plt.figure(figsize=(12, 4), num="maxent_rew")
- sns.heatmap(np.reshape(maxent_R, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- plt.savefig(full_path + "maxent_rew.jpg")
+ sns.heatmap(np.reshape(maxent_R_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
+ plt.savefig(full_path + "sim_maxent_R.jpg")
plt.figure(figsize=(12, 4), num="maxent_V")
- sns.heatmap(np.reshape(maxent_V, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- plt.savefig(full_path + "maxent_V.jpg")
+ sns.heatmap(np.reshape(maxent_V_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
+ plt.savefig(full_path + "sim_maxent_V.jpg")
plt.figure(figsize=(12, 4), num="maxent_P")
- sns.heatmap(np.reshape(maxent_P, (4, 12)), cmap="Spectral", annot=True, cbar=False)
- plt.savefig(full_path + "maxent_P.jpg")
+ sns.heatmap(np.reshape(maxent_P_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
+ plt.savefig(full_path + "sim_maxent_P.jpg")
+ #####################################################################################
+
+ #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"
+ 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)
+ else:
+ assert ("You're not using the user information")
+ question = input("Are you sure you don't want to load user's belief information?")
- #Compute entropy between two policies
- # policies = [exp_P, maxent_P]
- # entropy = get_entropy(policies, state_space_index, action_space_index)
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'],
@@ -288,17 +264,17 @@ def main():
var_agent_assistance_target_action=["agent_assistance"],
bn_model_agent_feedback=bn_model_caregiver_feedback,
var_agent_feedback_target_action=["agent_feedback"],
- agent_policy=maxent_P,
+ 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 = "AFTER_game_performance_" + "epoch_" + str(epochs) + "_persona_memory_" + str(
- persona_memory) + "_persona_attention_" + str(persona_attention) + "_persona_reactivity_" + str(
+ 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 = "AFTER_agent_assistance_" + "epoch_" + str(epochs) + "_persona_memory_" + str(
+ 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 = "AFTER_agent_feedback_" + "epoch_" + str(epochs) + "_persona_memory_" + str(
+ 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"
@@ -306,6 +282,25 @@ def main():
utils.plot2D_assistance(full_path + plot_agent_assistance_path, epochs, scaling_factor, agent_assistance_per_episode)
utils.plot2D_feedback(full_path + plot_agent_feedback_path, epochs, scaling_factor, agent_feedback_per_episode)
+ # R(s) and pi(s) generated from the first sim
+ maxent_R_real_sim = maxent(world, terminals, episodes_list)
+ maxent_V_real_sim, maxent_P_real_sim = vi.value_iteration(world.p_transition, maxent_R_real_sim, gamma=0.9, error=1e-3,
+ deterministic=True)
+ plt.figure(figsize=(12, 4), num="maxent_rew")
+ sns.heatmap(np.reshape(maxent_R_real_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
+ plt.savefig(full_path + "real_sim_maxent_R.jpg")
+ plt.figure(figsize=(12, 4), num="maxent_V")
+ sns.heatmap(np.reshape(maxent_V_real_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
+ plt.savefig(full_path + "real_sim_maxent_V.jpg")
+ plt.figure(figsize=(12, 4), num="maxent_P")
+ sns.heatmap(np.reshape(maxent_P_real_sim, (4, 12)), cmap="Spectral", annot=True, cbar=False)
+ plt.savefig(full_path + "real_sim_maxent_P.jpg")
+
+ # Compute entropy between two policies
+ policies = [maxent_P_sim, maxent_P_real_sim]
+ s_preferences, s_constraints = get_entropy(policies, state_space_index, action_space_index)
+ print("Preferences:", s_preferences, " Constraints:", s_constraints)
+
if __name__ == '__main__':
main()
\ No newline at end of file