agent = RequestInformSlotAgent(kb, act_set, slot_set, agent_params,
movie_request_slots, movie_inform_slots)
elif agt == 5: # movie request rule agent
agent = RequestBasicsAgent(kb, act_set, slot_set, agent_params,
movie_request_slots)
elif agt == 6: # restaurant request rule agent
agent = RequestBasicsAgent(kb, act_set, slot_set, agent_params,
restaurant_request_slots)
elif agt == 7: # taxi request agent
agent = RequestBasicsAgent(kb, act_set, slot_set, agent_params,
taxi_request_slots)
elif agt == 8: # taxi request-inform rule agent
agent = RequestInformSlotAgent(kb, act_set, slot_set, agent_params,
taxi_request_slots, taxi_inform_slots)
elif agt == 9: # DQN agent for movie domain
agent = AgentDQN(kb, act_set, slot_set, agent_params)
agent.initialize_config(movie_request_slots, movie_inform_slots)
elif agt == 10: # restaurant request-inform rule agent
agent = RequestInformSlotAgent(kb, act_set, slot_set, agent_params,
restaurant_request_slots,
restaurant_inform_slots)
elif agt == 11: # taxi request-inform-cost rule agent
agent = RequestInformSlotAgent(kb, act_set, slot_set, agent_params,
taxi_request_slots, taxi_inform_cost_slots)
elif agt == 12: # DQN agent for restaurant domain
load_actions(dialog_config.restaurant_sys_request_slots,
dialog_config.restaurant_sys_inform_slots)
agent = AgentDQN(kb, act_set, slot_set, agent_params)
agent.initialize_config(restaurant_request_slots, restaurant_inform_slots)
elif agt == 13: # DQN agent for taxi domain
load_actions(dialog_config.taxi_sys_request_slots,
agent_params['cmd_input_mode'] = params['cmd_input_mode']
if agt == 0:
agent = AgentCmd(movie_kb, act_set, slot_set, agent_params)
elif agt == 1:
agent = InformAgent(movie_kb, act_set, slot_set, agent_params)
elif agt == 2:
agent = RequestAllAgent(movie_kb, act_set, slot_set, agent_params)
elif agt == 3:
agent = RandomAgent(movie_kb, act_set, slot_set, agent_params)
elif agt == 4:
agent = EchoAgent(movie_kb, act_set, slot_set, agent_params)
elif agt == 5:
agent = RequestBasicsAgent(movie_kb, act_set, slot_set, agent_params)
elif agt == 9:
agent = AgentDQN(movie_kb, act_set, slot_set, agent_params)
################################################################################
# Add your agent here
################################################################################
else:
pass
################################################################################
# Parameters for User Simulators
################################################################################
usersim_params = {}
usersim_params['max_turn'] = max_turn
usersim_params['slot_err_probability'] = params['slot_err_prob']
usersim_params['slot_err_mode'] = params['slot_err_mode']
usersim_params['intent_err_probability'] = params['intent_err_prob']
if agt == 0:
agent = AgentCmd(course_kb, act_set, slot_set, agent_params)
elif agt == 1:
agent = InformAgent(course_kb, act_set, slot_set, agent_params)
elif agt == 2:
agent = RequestAllAgent(course_kb, act_set, slot_set, agent_params)
elif agt == 3:
agent = RandomAgent(course_kb, act_set, slot_set, agent_params)
elif agt == 4:
agent = EchoAgent(course_kb, act_set, slot_set, agent_params)
elif agt == 5:
agent = RequestBasicsAgent(course_kb, act_set, slot_set, agent_params)
elif agt == 9:
agent = AgentDQN(course_kb, act_set, slot_set, agent_params)
################################################################################
# Add your agent here
################################################################################
else:
pass
################################################################################
# Parameters for User Simulators
################################################################################
usersim_params = {}
usersim_params['max_turn'] = max_turn
usersim_params['slot_err_probability'] = params['slot_err_prob']
usersim_params['slot_err_mode'] = params['slot_err_mode']
usersim_params['intent_err_probability'] = params['intent_err_prob']
curr_user_goals = {}
if num_goals != 120:
curr_user_goals = select_random_user_goals(train_user_goals_no_req_slots, train_user_goals_with_req_slots,
training_user_goals_no_req_cardinality[i],
training_user_goals_req_cardinality[i])
else:
curr_user_goals["all"] = []
curr_user_goals["all"].extend(copy.deepcopy(train_user_goals))
# create pretrain user simulator
pretrain_user_sim = RuleSimulator(mock_dictionary, act_set, slot_set, copy.deepcopy(curr_user_goals), pretrain_usersim_params)
# create not a pre-trained user simulator
user_sim = RuleSimulator(mock_dictionary, act_set, slot_set, copy.deepcopy(curr_user_goals), usersim_params)
# create the pre-trained agent
pretrained_agent = AgentDQN(kb, act_set, slot_set, pretrained_agent_params)
# create the agent from scratch
agent = AgentDQN(kb, act_set, slot_set, agent_params)
# create dialogue manager for pre-trained agent
pretrain_dialog_manager = DialogManager(pretrained_agent, pretrain_user_sim, act_set, slot_set, kb)
# create dialogue manager for not pre-trained agent
dialog_manager = DialogManager(agent, user_sim, act_set, slot_set, kb)
# the warmup success rate of the pre-trained model
pretrain_warmup_succ_rate = 0
# the warmup success rate of the scratch model
warmup_succ_rate = 0
