Bug Fixes

.gitignore

@@ -4,3 +4,141 @@ public
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Pydial logs and models
+_*/
+*.log
+*.json
+*.dct
+*.prm
+*.pyc
+*.model
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# PyCharm stuff
+.idea/
+.xml
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

config/pydial_benchmarks/env1-dqn-CR.cfg

+# Error model: 0% error rate, addditive confscorer, uniform nbestgenerator
+# User model: standard sampled params, sampled patience
+# Masks: on
+
+###### General parameters ######
+[GENERAL]
+domains = CamRestaurants
+singledomain = True 
+tracedialog = 0
+seed = 07051991
+
+[exec_config]
+configdir = _benchmarkpolicies
+logfiledir = _benchmarklogs
+numtrainbatches = 4
+traindialogsperbatch = 1000
+numbatchtestdialogs =  500
+trainsourceiteration = 0
+numtestdialogs =  500
+trainerrorrate = 0
+testerrorrate  = 0
+testeverybatch = True
+#deleteprevpolicy = True
+
+[logging]
+usecolor = False
+screen_level = results
+file_level = results
+file = auto
+
+###### Environment parameters ######
+
+[agent]
+maxturns = 25
+
+[usermodel]
+usenewgoalscenarios = True
+oldstylepatience = False
+patience = 4,6
+configfile = config/sampledUM.cfg
+
+[errormodel]
+nbestsize = 1
+confusionmodel = RandomConfusions
+nbestgeneratormodel = SampledNBestGenerator
+confscorer = additive
+
+
+[summaryacts]
+maxinformslots = 5
+informmask = True
+requestmask = True
+informcountaccepted = 4
+byemask = True
+
+###### Dialogue Manager parameters ######
+
+## Comment the following lines if using any other policy (this uses handcrafted policy)##
+# [policy]
+# policydir = _benchmarkpolicies
+# belieftype = focus
+# useconfreq = False
+# learning = True
+# policytype = hdc
+# startwithhello = False
+# inpolicyfile = auto
+# outpolicyfile = auto
+
+## Uncomment for GP policy ##
+#[policy]
+#policydir = _benchmarkpolicies
+#belieftype = focus
+#useconfreq = False
+#learning = True
+#policytype = gp
+#startwithhello = False
+#inpolicyfile = auto
+#outpolicyfile = auto
+#
+#[gppolicy]
+#kernel = polysort
+#
+#[gpsarsa]
+#random = False
+#scale = 3
+
+## Uncomment for DQN policy ##
+[policy]
+policydir = _benchmarkpolicies
+belieftype = focus
+useconfreq = False
+learning = True
+policytype = dqn
+startwithhello = False
+inpolicyfile = auto
+outpolicyfile = auto
+
+[dqnpolicy]
+maxiter = 4000
+gamma = 0.99
+learning_rate = 0.001
+tau = 0.02
+replay_type = vanilla
+minibatch_size = 64
+capacity = 6000
+exploration_type = e-greedy
+episodeNum= 0.0
+epsilon_start = 0.3
+epsilon_end = 0.0
+n_in = 268
+features = ["discourseAct", "method", "requested", "full", "lastActionInformNone", "offerHappened", "inform_info"]
+max_k = 5
+learning_algorithm = dqn
+architecture = vanilla
+h1_size = 300
+h2_size = 100
+training_frequency = 2
+n_samples = 1
+stddev_var_mu = 0.01
+stddev_var_logsigma = 0.01
+mean_log_sigma = 0.000001
+sigma_prior = 1.5
+alpha =0.85
+alpha_divergence =False
+sigma_eps = 0.01
+delta = 1.0
+beta = 0.95
+is_threshold = 5.0
+train_iters_per_episode = 1
+
+## Uncomment for A2C policy ##
+#[policy]
+#policydir = _benchmarkpolicies
+#belieftype = focus
+#useconfreq = False
+#learning = True
+#policytype = a2c
+#startwithhello = False
+#inpolicyfile = auto
+#outpolicyfile = auto
+
+#[dqnpolicy]
+#maxiter = 4000
+#gamma = 0.99
+#learning_rate = 0.001
+#tau = 0.02
+#replay_type = vanilla
+#minibatch_size = 64
+#capacity = 1000
+#exploration_type = e-greedy
+#episodeNum= 0.0
+#epsilon_start = 0.5
+#epsilon_end = 0.0
+#n_in = 268
+#features = ["discourseAct", "method", "requested", "full", "lastActionInformNone", "offerHappened", "inform_info"]
+#max_k = 5
+#learning_algorithm = dqn
+#architecture = vanilla
+#h1_size = 200
+#h2_size = 75
+#training_frequency = 2
+#n_samples = 1
+#stddev_var_mu = 0.01
+#stddev_var_logsigma = 0.01
+#mean_log_sigma = 0.000001
+#sigma_prior = 1.5
+#alpha =0.85
+#alpha_divergence =False
+#sigma_eps = 0.01
+#delta = 1.0
+#beta = 0.95
+#is_threshold = 5.0
+#train_iters_per_episode = 1
+
+## Uncomment for eNAC policy ##
+#[policy]
+#policydir = _benchmarkpolicies
+#belieftype = focus
+#useconfreq = False
+#learning = True
+#policytype = enac
+#startwithhello = False
+#inpolicyfile = auto
+#outpolicyfile = auto
+
+#[dqnpolicy]
+#maxiter = 4000
+#gamma = 0.99
+#learning_rate = 0.001
+#tau = 0.02
+#replay_type = vanilla
+#minibatch_size = 64
+#capacity = 1000
+#exploration_type = e-greedy
+#episodeNum= 0.0
+#epsilon_start = 0.3
+#epsilon_end = 0.0
+#n_in = 268
+#features = ["discourseAct", "method", "requested", "full", "lastActionInformNone", "offerHappened", "inform_info"]
+#max_k = 5
+#learning_algorithm = dqn
+#architecture = vanilla
+#h1_size = 130
+#h2_size = 50
+#training_frequency = 2
+#n_samples = 1
+#stddev_var_mu = 0.01
+#stddev_var_logsigma = 0.01
+#mean_log_sigma = 0.000001
+#sigma_prior = 1.5
+#alpha =0.85
+#alpha_divergence =False
+#sigma_eps = 0.01
+#delta = 1.0
+#beta = 0.95
+#is_threshold = 5.0
+#train_iters_per_episode = 1
+
+###### Evaluation parameters ######
+
+[eval]
+rewardvenuerecommended=0
+penaliseallturns = True
+wrongvenuepenalty = 0
+notmentionedvaluepenalty = 0      
+successmeasure = objective 
+successreward = 20
+

policy/DQNPolicy.py

@@ -768,7 +768,7 @@ class DQNPolicy(Policy.Policy):
                 curiosity_loss = self.curiosityFunctions.training(s2_batch, s_batch, a_batch_one_hot)
                 # self.curiositypred_loss.append(curiosity_loss)  # for plotting
 
-            predicted_q_value, currentLoss = self.dqn.train(s_batch, a_batch_one_hot, reshaped_yi)
+            predicted_q_value, _, currentLoss = self.dqn.train(s_batch, a_batch_one_hot, reshaped_yi)
 
             if self.episodecount % 1 == 0:
                 # Update target networks

pydial.py

@@ -931,6 +931,9 @@ def test_command(configfile, iteration, seed=None, testerrorrate=None, trainerro
                     policyname = '-'.join(ps[:-1] + ['seed{}'.format(orig_seed)] + [ps[-1]])
             else:
                 policyname = "%s-%02d.%d" % (configId, gtrainerrorrate, i)
+                if not 'seed' in policyname:
+                    ps= policyname.split('-')
+                    policyname = '-'.join(ps[:-1] + ['seed{}'.format(orig_seed)] + [ps[-1]])
             poldirpath = path(policy_dir)
             if poldirpath.isdir():
                 policyfiles = poldirpath.files()