interact with human

pydial/Agent.py

@@ -202,6 +202,7 @@ def start_call(self, session_id, domainSimulatedUsers=None, maxNumTurnsScaling=1
 
         # SEMO:
         self.prompt_str = self._agents_semo(sys_act)
+
 
         self.callValidator.validate(sys_act)
 
@@ -268,20 +269,22 @@ def continue_call(self, asr_info, domainString=None, domainSimulatedUsers=None,
 
         state = self.semi_belief_manager.update_belief_state(ASR_obs=asr_info, sys_act=prev_sys_act,
                                                      dstring=currentDomain, turn=self.currentTurn,hub_id = self.hub_id, sim_lvl=self.sim_level)
-        
+
         self._print_usr_act(state, currentDomain)
-        
+
         # 2. Policy -- Determine system act/response
         sys_act = self.policy_manager.act_on(dstring=currentDomain, 
                                                   state=state, preference=preference)
 
         # Check ending the call:
         sys_act = self._check_ENDING_CALL(state, sys_act)  # NB: this may change the self.prompt_str
-
-        self._print_sys_act(sys_act)
-
+        
+        self._print_sys_act(sys_act)        
+        
         # SEMO:
+        # print(sys_act)
         self.prompt_str = self._agents_semo(sys_act)
+
         sys_act.prompt = self.prompt_str
 
 

pydial/Simulate.py

@@ -130,11 +130,11 @@ def run_dialogs(self, numDialogs):
         '''
         for i in range(numDialogs):
             logger.info('Dialogue %d' % (i+1))
-            self.run(session_id='simulate_dialog'+str(i), sim_level=self.sim_level, roi=roi)
+            self.run(session_id='simulate_dialog'+str(i), sim_level=self.sim_level)
 
         self.agent_factory.power_down_factory() # Important! -uses FORCE_SAVE on policy- which will finalise learning and save policy.
 
-    def run(self, session_id, agent_id='Smith', sim_level='dial_act', roi=False):
+    def run(self, session_id, agent_id='Smith', sim_level='dial_act'):
         '''
         Runs one episode through the simulator
         
@@ -150,8 +150,6 @@ def run(self, session_id, agent_id='Smith', sim_level='dial_act', roi=False):
 
         preference = torch.randn(2)
         preference = (torch.abs(preference) / torch.norm(preference, p=1)).type(FloatTensor)
-        if roi:
-            pass
         logger.dial('User\'s preference: [{}, {}]'.format(preference[0], preference[1]))
 
         # RESET THE USER SIMULATOR:

pydial/Texthub.py

@@ -58,6 +58,8 @@
 '''
 import argparse, re
 
+import torch
+
 from Agent import DialogueAgent
 from utils import ContextLogger
 from utils import Settings
@@ -67,6 +69,9 @@
 __author__ = "cued_dialogue_systems_group"
 __version__ = Settings.__version__
 
+use_cuda = torch.cuda.is_available()
+FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
+
 class ConsoleHub(object):
     '''
     text based dialog system
@@ -77,15 +82,25 @@ def __init__(self):
         #-----------------------------------------
         self.agent = DialogueAgent(hub_id='texthub')
 
-    def run(self):
+    def run(self, domain):
         '''
         Runs one episode through Hub
 
         :returns: None
         '''
 
+        # GENERATE A USER PREFERENCE: a * Length + (1-a) * Success
+
+
+        preference = torch.randn(2)
+        preference = (torch.abs(preference) / torch.norm(preference, p=1)).type(FloatTensor)
+        logger.dial('User\'s preference: [{}, {}]'.format(preference[0], preference[1]))
+        print 'User\'s preference: [Brevity: {}, Success: {}]'.format(preference[0], preference[1])
+
         logger.warning("NOTE: texthub is not using any error simulation at present.")
-        sys_act = self.agent.start_call(session_id='texthub_dialog')
+        sys_act = self.agent.start_call(session_id='texthub_dialog',
+                                        preference=preference)
+
         print 'Prompt > ' + sys_act.prompt
         while not self.agent.ENDING_DIALOG:
             # USER ACT:
@@ -107,15 +122,16 @@ def run(self):
                 #     print 'Semi > null() [0.001]'
                 #--------------------------------
             '''
-            domain = None
-            if "domain(" in obs:
-                match = re.search("(.*)(domain\()([^\)]+)(\))(.*)",obs)
-                if match is not None:
-                    domain = match.group(3)
-                    obs = match.group(1) + match.group(5)
+
+            # domain = None
+            # if "domain(" in obs:
+            #     match = re.search("(.*)(domain\()([^\)]+)(\))(.*)",obs)
+            #     if match is not None:
+            #         domain = match.group(3)
+            #         obs = match.group(1) + match.group(5)
 
             # SYSTEM ACT:
-            sys_act = self.agent.continue_call(asr_info = [(obs,1.0)], domainString = domain)
+            sys_act = self.agent.continue_call(asr_info = [(obs,1.0)], domainString = domain, preference=preference)
             print 'Prompt > ' + sys_act.prompt
 
         # Process ends. -----------------------------------------------------

pydial/_morlconfigs/envelope-00.1.train.cfg

@@ -0,0 +1,89 @@
+[GENERAL]
+domains = CamRestaurants
+singledomain = True
+tracedialog = 0
+seed = 30061522
+
+[exec_config]
+domain = CamRestaurants
+configdir = _morlconfigs
+logfiledir = _morllogs
+numtrainbatches = 5
+traindialogsperbatch = 1000
+numbatchtestdialogs = 10
+trainsourceiteration = 0
+numtestdialogs = 100
+trainerrorrate = 0
+testerrorrate = 0
+testeverybatch = False
+
+[logging]
+usecolor = False
+screen_level = results
+file_level = dial
+file = _morllogs/envelope-seed30061522-00.1-5.train.log
+
+[agent]
+maxturns = 25
+
+[usermodel]
+usenewgoalscenarios = True
+oldstylepatience = False
+patience = 5
+configfile = config/sampledUM.cfg
+
+[errormodel]
+nbestsize = 1
+nbestgeneratormodel = SampledNBestGenerator
+confscorer = additive
+
+[summaryacts]
+maxinformslots = 5
+informmask = True
+requestmask = True
+informcountaccepted = 4
+byemask = True
+
+[policy]
+policydir = _morlpolicies
+belieftype = focus
+useconfreq = False
+learning = True
+policytype = morl
+startwithhello = False
+inpolicyfile = _morlpolicies/envelope-00.0
+outpolicyfile = _morlpolicies/envelope-00.1
+
+[morlpolicy]
+n_rew = 2
+learning_rate = 0.001
+epsilon = 0.5
+epsilon_decay = True
+gamma = 0.999
+batch_size = 64
+weight_num = 32
+mem_size = 1000
+episode_num = 1000
+optimizer = Adam
+save_step = 100
+update_freq = 50
+training_freq = 1
+algorithm = envelope
+beta = 0.9
+homotopy = True
+
+[gppolicy]
+kernel = polysort
+
+[gpsarsa]
+random = False
+scale = 3
+
+[eval]
+rewardvenuerecommended = 0
+penaliseallturns = True
+wrongvenuepenalty = 0
+notmentionedvaluepenalty = 0
+successmeasure = objective
+successreward = 20
+

pydial/_morlconfigs/envelope-00.2.train.cfg

@@ -0,0 +1,89 @@
+[GENERAL]
+domains = CamRestaurants
+singledomain = True
+tracedialog = 0
+seed = 30061522
+
+[exec_config]
+domain = CamRestaurants
+configdir = _morlconfigs
+logfiledir = _morllogs
+numtrainbatches = 5
+traindialogsperbatch = 1000
+numbatchtestdialogs = 10
+trainsourceiteration = 0
+numtestdialogs = 100
+trainerrorrate = 0
+testerrorrate = 0
+testeverybatch = False
+
+[logging]
+usecolor = False
+screen_level = results
+file_level = dial
+file = _morllogs/envelope-seed30061522-00.1-5.train.log
+
+[agent]
+maxturns = 25
+
+[usermodel]
+usenewgoalscenarios = True
+oldstylepatience = False
+patience = 5
+configfile = config/sampledUM.cfg
+
+[errormodel]
+nbestsize = 1
+nbestgeneratormodel = SampledNBestGenerator
+confscorer = additive
+
+[summaryacts]
+maxinformslots = 5
+informmask = True
+requestmask = True
+informcountaccepted = 4
+byemask = True
+
+[policy]
+policydir = _morlpolicies
+belieftype = focus
+useconfreq = False
+learning = True
+policytype = morl
+startwithhello = False
+inpolicyfile = _morlpolicies/envelope-00.1
+outpolicyfile = _morlpolicies/envelope-00.2
+
+[morlpolicy]
+n_rew = 2
+learning_rate = 0.001
+epsilon = 0.5
+epsilon_decay = True
+gamma = 0.999
+batch_size = 64
+weight_num = 32
+mem_size = 1000
+episode_num = 1000
+optimizer = Adam
+save_step = 100
+update_freq = 50
+training_freq = 1
+algorithm = envelope
+beta = 0.9
+homotopy = True
+
+[gppolicy]
+kernel = polysort
+
+[gpsarsa]
+random = False
+scale = 3
+
+[eval]
+rewardvenuerecommended = 0
+penaliseallturns = True
+wrongvenuepenalty = 0
+notmentionedvaluepenalty = 0
+successmeasure = objective
+successreward = 20
+

pydial/_morlconfigs/envelope-00.3.train.cfg

@@ -0,0 +1,89 @@
+[GENERAL]
+domains = CamRestaurants
+singledomain = True
+tracedialog = 0
+seed = 30061522
+
+[exec_config]
+domain = CamRestaurants
+configdir = _morlconfigs
+logfiledir = _morllogs
+numtrainbatches = 5
+traindialogsperbatch = 1000
+numbatchtestdialogs = 10
+trainsourceiteration = 0
+numtestdialogs = 100
+trainerrorrate = 0
+testerrorrate = 0
+testeverybatch = False
+
+[logging]
+usecolor = False
+screen_level = results
+file_level = dial
+file = _morllogs/envelope-seed30061522-00.1-5.train.log
+
+[agent]
+maxturns = 25
+
+[usermodel]
+usenewgoalscenarios = True
+oldstylepatience = False
+patience = 5
+configfile = config/sampledUM.cfg
+
+[errormodel]
+nbestsize = 1
+nbestgeneratormodel = SampledNBestGenerator
+confscorer = additive
+
+[summaryacts]
+maxinformslots = 5
+informmask = True
+requestmask = True
+informcountaccepted = 4
+byemask = True
+
+[policy]
+policydir = _morlpolicies
+belieftype = focus
+useconfreq = False
+learning = True
+policytype = morl
+startwithhello = False
+inpolicyfile = _morlpolicies/envelope-00.2
+outpolicyfile = _morlpolicies/envelope-00.3
+
+[morlpolicy]
+n_rew = 2
+learning_rate = 0.001
+epsilon = 0.5
+epsilon_decay = True
+gamma = 0.999
+batch_size = 64
+weight_num = 32
+mem_size = 1000
+episode_num = 1000
+optimizer = Adam
+save_step = 100
+update_freq = 50
+training_freq = 1
+algorithm = envelope
+beta = 0.9
+homotopy = True
+
+[gppolicy]
+kernel = polysort
+
+[gpsarsa]
+random = False
+scale = 3
+
+[eval]
+rewardvenuerecommended = 0
+penaliseallturns = True
+wrongvenuepenalty = 0
+notmentionedvaluepenalty = 0
+successmeasure = objective
+successreward = 20
+