def __init__(self,
in_policy_file,
out_policy_file,
domainString='CamRestaurants',
is_training=False):
super(ACERPolicy, self).__init__(domainString, is_training)
tf.reset_default_graph()
self.in_policy_file = in_policy_file
self.out_policy_file = out_policy_file
self.is_training = is_training
self.accum_belief = []
self.prev_state_check = None
self.domainString = domainString
self.domainUtil = FlatOnt.FlatDomainOntology(self.domainString)
#improvement==================================
self.intrinsic_reward_method = None
if cfg.has_option('scme', 'method'):
self.intrinsic_reward_method = cfg.get('scme', 'method')
#improvement==================================
# parameter settings
if 0: # cfg.has_option('dqnpolicy', 'n_in'): #ic304: this was giving me a weird error, disabled it until i can check it deeper
self.n_in = cfg.getint('dqnpolicy', 'n_in')
else:
self.n_in = self.get_n_in(domainString)
self.actor_lr = 0.0001
if cfg.has_option('dqnpolicy', 'actor_lr'):
self.actor_lr = cfg.getfloat('dqnpolicy', 'actor_lr')
self.critic_lr = 0.001
if cfg.has_option('dqnpolicy', 'critic_lr'):
self.critic_lr = cfg.getfloat('dqnpolicy', 'critic_lr')
self.delta = 1.
if cfg.has_option('dqnpolicy', 'delta'):
self.delta = cfg.getfloat('dqnpolicy', 'delta')
self.alpha = 0.99
if cfg.has_option('dqnpolicy', 'beta'):
self.alpha = cfg.getfloat('dqnpolicy', 'beta')
self.c = 10.
if cfg.has_option('dqnpolicy', 'is_threshold'):
self.c = cfg.getfloat('dqnpolicy', 'is_threshold')
self.randomseed = 1234
if cfg.has_option('GENERAL', 'seed'):
self.randomseed = cfg.getint('GENERAL', 'seed')
self.gamma = 0.99
if cfg.has_option('dqnpolicy', 'gamma'):
self.gamma = cfg.getfloat('dqnpolicy', 'gamma')
self.regularisation = 'l2'
if cfg.has_option('dqnpolicy', 'regularisation'):
self.regularisation = cfg.get('dqnpolicy', 'regularisation')
self.learning_rate = 0.001
if cfg.has_option('dqnpolicy', 'learning_rate'):
self.learning_rate = cfg.getfloat('dqnpolicy', 'learning_rate')
self.exploration_type = 'e-greedy' # Boltzman
if cfg.has_option('dqnpolicy', 'exploration_type'):
self.exploration_type = cfg.get('dqnpolicy', 'exploration_type')
self.episodeNum = 1000
if cfg.has_option('dqnpolicy', 'episodeNum'):
self.episodeNum = cfg.getfloat('dqnpolicy', 'episodeNum')
self.maxiter = 5000
if cfg.has_option('dqnpolicy', 'maxiter'):
self.maxiter = cfg.getfloat('dqnpolicy', 'maxiter')
self.curiosityreward = False
if cfg.has_option('eval', 'curiosityreward'):
self.curiosityreward = cfg.getboolean('eval', 'curiosityreward')
self.epsilon = 1
if cfg.has_option('dqnpolicy', 'epsilon'):
self.epsilon = cfg.getfloat('dqnpolicy', 'epsilon')
if not self.curiosityreward: # no eps-greedy exploration when curious expl. is used
self.epsilon_start = 1
if cfg.has_option('dqnpolicy', 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy', 'epsilon_start')
else:
self.epsilon_start = 1
if cfg.has_option('dqnpolicy', 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy', 'epsilon_start')
self.epsilon_end = 1
if cfg.has_option('dqnpolicy', 'epsilon_end'):
self.epsilon_end = cfg.getfloat('dqnpolicy', 'epsilon_end')
self.priorProbStart = 1.0
if cfg.has_option('dqnpolicy', 'prior_sample_prob_start'):
self.priorProbStart = cfg.getfloat('dqnpolicy',
'prior_sample_prob_start')
self.priorProbEnd = 0.1
if cfg.has_option('dqnpolicy', 'prior_sample_prob_end'):
self.priorProbEnd = cfg.getfloat('dqnpolicy',
'prior_sample_prob_end')
self.policyfeatures = []
if cfg.has_option('dqnpolicy', 'features'):
logger.info('Features: ' + str(cfg.get('dqnpolicy', 'features')))
self.policyfeatures = json.loads(cfg.get('dqnpolicy', 'features'))
self.max_k = 5
if cfg.has_option('dqnpolicy', 'max_k'):
self.max_k = cfg.getint('dqnpolicy', 'max_k')
self.learning_algorithm = 'drl'
if cfg.has_option('dqnpolicy', 'learning_algorithm'):
self.learning_algorithm = cfg.get('dqnpolicy',
'learning_algorithm')
logger.info('Learning algorithm: ' + self.learning_algorithm)
self.minibatch_size = 32
if cfg.has_option('dqnpolicy', 'minibatch_size'):
self.minibatch_size = cfg.getint('dqnpolicy', 'minibatch_size')
self.capacity = 1000
if cfg.has_option('dqnpolicy', 'capacity'):
self.capacity = cfg.getint('dqnpolicy', 'capacity')
self.replay_type = 'vanilla'
if cfg.has_option('dqnpolicy', 'replay_type'):
self.replay_type = cfg.get('dqnpolicy', 'replay_type')
self.architecture = 'vanilla'
if cfg.has_option('dqnpolicy', 'architecture'):
self.architecture = cfg.get('dqnpolicy', 'architecture')
self.q_update = 'single'
if cfg.has_option('dqnpolicy', 'q_update'):
self.q_update = cfg.get('dqnpolicy', 'q_update')
self.h1_size = 130
if cfg.has_option('dqnpolicy', 'h1_size'):
self.h1_size = cfg.getint('dqnpolicy', 'h1_size')
self.h2_size = 50
if cfg.has_option('dqnpolicy', 'h2_size'):
self.h2_size = cfg.getint('dqnpolicy', 'h2_size')
self.save_step = 200
if cfg.has_option('policy', 'save_step'):
self.save_step = cfg.getint('policy', 'save_step')
self.importance_sampling = 'soft'
if cfg.has_option('dqnpolicy', 'importance_sampling'):
self.importance_sampling = cfg.get('dqnpolicy',
'importance_sampling')
self.train_iters_per_episode = 1
if cfg.has_option('dqnpolicy', 'train_iters_per_episode'):
self.train_iters_per_episode = cfg.getint(
'dqnpolicy', 'train_iters_per_episode')
self.training_frequency = 2
if cfg.has_option('dqnpolicy', 'training_frequency'):
self.training_frequency = cfg.getint('dqnpolicy',
'training_frequency')
# domain specific parameter settings (overrides general policy parameter settings)
if cfg.has_option('dqnpolicy_' + domainString, 'n_in'):
self.n_in = cfg.getint('dqnpolicy_' + domainString, 'n_in')
if cfg.has_option('dqnpolicy_' + domainString, 'actor_lr'):
self.actor_lr = cfg.getfloat('dqnpolicy_' + domainString,
'actor_lr')
if cfg.has_option('dqnpolicy_' + domainString, 'critic_lr'):
self.critic_lr = cfg.getfloat('dqnpolicy_' + domainString,
'critic_lr')
if cfg.has_option('dqnpolicy_' + domainString, 'delta'):
self.delta = cfg.getfloat('dqnpolicy_' + domainString, 'delta')
if cfg.has_option('dqnpolicy_' + domainString, 'beta'):
self.alpha = cfg.getfloat('dqnpolicy_' + domainString, 'beta')
if cfg.has_option('dqnpolicy_' + domainString, 'is_threshold'):
self.c = cfg.getfloat('dqnpolicy_' + domainString, 'is_threshold')
if cfg.has_option('dqnpolicy_' + domainString, 'gamma'):
self.gamma = cfg.getfloat('dqnpolicy_' + domainString, 'gamma')
if cfg.has_option('dqnpolicy_' + domainString, 'regularisation'):
self.regularisation = cfg.get('dqnpolicy_' + domainString,
'regulariser')
if cfg.has_option('dqnpolicy_' + domainString, 'learning_rate'):
self.learning_rate = cfg.getfloat('dqnpolicy_' + domainString,
'learning_rate')
if cfg.has_option('dqnpolicy_' + domainString, 'exploration_type'):
self.exploration_type = cfg.get('dqnpolicy_' + domainString,
'exploration_type')
if cfg.has_option('dqnpolicy_' + domainString, 'episodeNum'):
self.episodeNum = cfg.getfloat('dqnpolicy_' + domainString,
'episodeNum')
if cfg.has_option('dqnpolicy_' + domainString, 'maxiter'):
self.maxiter = cfg.getfloat('dqnpolicy_' + domainString, 'maxiter')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon'):
self.epsilon = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy_' + domainString,
'epsilon_start')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon_end'):
self.epsilon_end = cfg.getfloat('dqnpolicy_' + domainString,
'epsilon_end')
if cfg.has_option('dqnpolicy_' + domainString,
'prior_sample_prob_start'):
self.priorProbStart = cfg.getfloat('dqnpolicy_' + domainString,
'prior_sample_prob_start')
if cfg.has_option('dqnpolicy_' + domainString,
'prior_sample_prob_end'):
self.priorProbEnd = cfg.getfloat('dqnpolicy_' + domainString,
'prior_sample_prob_end')
if cfg.has_option('dqnpolicy_' + domainString, 'features'):
logger.info('Features: ' +
str(cfg.get('dqnpolicy_' + domainString, 'features')))
self.policyfeatures = json.loads(
cfg.get('dqnpolicy_' + domainString, 'features'))
if cfg.has_option('dqnpolicy_' + domainString, 'max_k'):
self.max_k = cfg.getint('dqnpolicy_' + domainString, 'max_k')
if cfg.has_option('dqnpolicy_' + domainString, 'learning_algorithm'):
self.learning_algorithm = cfg.get('dqnpolicy_' + domainString,
'learning_algorithm')
logger.info('Learning algorithm: ' + self.learning_algorithm)
if cfg.has_option('dqnpolicy_' + domainString, 'minibatch_size'):
self.minibatch_size = cfg.getint('dqnpolicy_' + domainString,
'minibatch_size')
if cfg.has_option('dqnpolicy_' + domainString, 'capacity'):
self.capacity = cfg.getint('dqnpolicy_' + domainString, 'capacity')
if cfg.has_option('dqnpolicy_' + domainString, 'replay_type'):
self.replay_type = cfg.get('dqnpolicy_' + domainString,
'replay_type')
if cfg.has_option('dqnpolicy_' + domainString, 'architecture'):
self.architecture = cfg.get('dqnpolicy_' + domainString,
'architecture')
if cfg.has_option('dqnpolicy_' + domainString, 'q_update'):
self.q_update = cfg.get('dqnpolicy_' + domainString, 'q_update')
if cfg.has_option('dqnpolicy_' + domainString, 'h1_size'):
self.h1_size = cfg.getint('dqnpolicy_' + domainString, 'h1_size')
if cfg.has_option('dqnpolicy_' + domainString, 'h2_size'):
self.h2_size = cfg.getint('dqnpolicy_' + domainString, 'h2_size')
if cfg.has_option('policy_' + domainString, 'save_step'):
self.save_step = cfg.getint('policy_' + domainString, 'save_step')
if cfg.has_option('dqnpolicy_' + domainString, 'importance_sampling'):
self.importance_sampling = cfg.get('dqnpolicy_' + domainString,
'importance_sampling')
if cfg.has_option('dqnpolicy_' + domainString,
'train_iters_per_episode'):
self.train_iters_per_episode = cfg.getint(
'dqnpolicy_' + domainString, 'train_iters_per_episode')
if cfg.has_option('dqnpolicy_' + domainString, 'training_frequency'):
self.training_frequency = cfg.getint('dqnpolicy_' + domainString,
'training_frequency')
self.episode_ct = 0
self.episode_ave_max_q = []
self.mu_prob = 0. # behavioral policy
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# init session
self.sess = tf.Session()
with tf.device("/cpu:0"):
np.random.seed(self.randomseed)
tf.set_random_seed(self.randomseed)
# initialise an replay buffer
if self.replay_type == 'vanilla':
self.episodes[self.domainString] = ReplayBufferEpisode(
self.capacity, self.minibatch_size, self.randomseed)
elif self.replay_type == 'prioritized':
self.episodes[self.domainString] = ReplayPrioritisedEpisode(
self.capacity, self.minibatch_size, self.randomseed)
#replay_buffer = ReplayBuffer(self.capacity, self.randomseed)
#self.episodes = []
self.samplecount = 0
self.episodecount = 0
# construct the models
self.state_dim = self.n_in
self.summaryaction = SummaryAction.SummaryAction(domainString)
self.action_dim = len(self.summaryaction.action_names)
action_bound = len(self.summaryaction.action_names)
self.stats = [0 for _ in range(self.action_dim)]
self.global_mu = [0. for _ in range(self.action_dim)]
self.acer = acer.ACERNetwork(self.sess, self.state_dim, self.action_dim, \
self.critic_lr, self.delta, self.c, self.alpha, self.h1_size, self.h2_size, self.is_training, self.randomseed)
# when all models are defined, init all variables
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
self.loadPolicy(self.in_policy_file)
print 'loaded replay size: ', self.episodes[
self.domainString].size()
#improvement==================================
#initial
if self.intrinsic_reward_method == 'vime':
self.vime_model = vime(self.state_dim, self.action_dim)
self.vime_model.load_model('model/vime_model/' +
self.in_policy_file)
elif self.intrinsic_reward_method == 'cme':
self.cme_model = cme(self.state_dim, self.action_dim)
self.cme_model.load_model('model/cme_model/' +
self.in_policy_file)
elif self.intrinsic_reward_method == 'scme':
self.scme_model = scme(self.state_dim, self.action_dim)
self.scme_model.load_model('model/scme_model/' +
self.in_policy_file)
def __init__(self, in_policy_file, out_policy_file, domainString='CamRestaurants', is_training=False):
super(ENACPolicy, self).__init__(domainString, is_training)
tf.reset_default_graph()
self.in_policy_file = in_policy_file
self.out_policy_file = out_policy_file
self.is_training = is_training
self.accum_belief = []
self.prev_state_check = None
self.domainString = domainString
self.domainUtil = FlatOnt.FlatDomainOntology(self.domainString)
# parameter settings
if 0:#cfg.has_option('dqnpolicy', 'n_in'): #ic304: this was giving me a weird error, disabled it until i can check it deeper
self.n_in = cfg.getint('dqnpolicy', 'n_in')
else:
self.n_in = self.get_n_in(domainString)
self.actor_lr = 0.0001
if cfg.has_option('dqnpolicy', 'actor_lr'):
self.actor_lr = cfg.getfloat('dqnpolicy', 'actor_lr')
self.critic_lr = 0.001
if cfg.has_option('dqnpolicy', 'critic_lr'):
self.critic_lr = cfg.getfloat('dqnpolicy', 'critic_lr')
self.tau = 0.001
if cfg.has_option('dqnpolicy', 'tau'):
self.tau = cfg.getfloat('dqnpolicy', 'tau')
self.randomseed = 1234
if cfg.has_option('GENERAL', 'seed'):
self.randomseed = cfg.getint('GENERAL', 'seed')
self.gamma = 1.0
if cfg.has_option('dqnpolicy', 'gamma'):
self.gamma = cfg.getfloat('dqnpolicy', 'gamma')
self.regularisation = 'l2'
if cfg.has_option('dqnpolicy', 'regularisation'):
self.regularisation = cfg.get('dqnpolicy', 'regulariser')
self.learning_rate = 0.001
if cfg.has_option('dqnpolicy', 'learning_rate'):
self.learning_rate = cfg.getfloat('dqnpolicy', 'learning_rate')
self.exploration_type = 'e-greedy' # Boltzman
if cfg.has_option('dqnpolicy', 'exploration_type'):
self.exploration_type = cfg.get('dqnpolicy', 'exploration_type')
self.episodeNum = 1000
if cfg.has_option('dqnpolicy', 'episodeNum'):
self.episodeNum = cfg.getfloat('dqnpolicy', 'episodeNum')
self.maxiter = 5000
if cfg.has_option('dqnpolicy', 'maxiter'):
self.maxiter = cfg.getfloat('dqnpolicy', 'maxiter')
self.epsilon = 1
if cfg.has_option('dqnpolicy', 'epsilon'):
self.epsilon = cfg.getfloat('dqnpolicy', 'epsilon')
self.epsilon_start = 1
if cfg.has_option('dqnpolicy', 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy', 'epsilon_start')
self.epsilon_end = 1
if cfg.has_option('dqnpolicy', 'epsilon_end'):
self.epsilon_end = cfg.getfloat('dqnpolicy', 'epsilon_end')
self.priorProbStart = 1.0
if cfg.has_option('dqnpolicy', 'prior_sample_prob_start'):
self.priorProbStart = cfg.getfloat('dqnpolicy', 'prior_sample_prob_start')
self.priorProbEnd = 0.1
if cfg.has_option('dqnpolicy', 'prior_sample_prob_end'):
self.priorProbEnd = cfg.getfloat('dqnpolicy', 'prior_sample_prob_end')
self.policyfeatures = []
if cfg.has_option('dqnpolicy', 'features'):
logger.info('Features: ' + str(cfg.get('dqnpolicy', 'features')))
self.policyfeatures = json.loads(cfg.get('dqnpolicy', 'features'))
self.max_k = 5
if cfg.has_option('dqnpolicy', 'max_k'):
self.max_k = cfg.getint('dqnpolicy', 'max_k')
self.learning_algorithm = 'drl'
if cfg.has_option('dqnpolicy', 'learning_algorithm'):
self.learning_algorithm = cfg.get('dqnpolicy', 'learning_algorithm')
logger.info('Learning algorithm: ' + self.learning_algorithm)
self.minibatch_size = 32
if cfg.has_option('dqnpolicy', 'minibatch_size'):
self.minibatch_size = cfg.getint('dqnpolicy', 'minibatch_size')
self.capacity = 1000 # max(self.minibatch_size, 2000)
if cfg.has_option('dqnpolicy', 'capacity'):
self.capacity = cfg.getint('dqnpolicy', 'capacity')
self.replay_type = 'vanilla'
if cfg.has_option('dqnpolicy', 'replay_type'):
self.replay_type = cfg.get('dqnpolicy', 'replay_type')
self.architecture = 'vanilla'
if cfg.has_option('dqnpolicy', 'architecture'):
self.architecture = cfg.get('dqnpolicy', 'architecture')
self.q_update = 'single'
if cfg.has_option('dqnpolicy', 'q_update'):
self.q_update = cfg.get('dqnpolicy', 'q_update')
self.h1_size = 130
if cfg.has_option('dqnpolicy', 'h1_size'):
self.h1_size = cfg.getint('dqnpolicy', 'h1_size')
self.h2_size = 50
if cfg.has_option('dqnpolicy', 'h2_size'):
self.h2_size = cfg.getint('dqnpolicy', 'h2_size')
self.save_step = 200
if cfg.has_option('policy', 'save_step'):
self.save_step = cfg.getint('policy', 'save_step')
self.importance_sampling = 'soft'
if cfg.has_option('dqnpolicy', 'importance_sampling'):
self.importance_sampling = cfg.get('dqnpolicy', 'importance_sampling')
self.training_frequency = 2
if cfg.has_option('dqnpolicy', 'training_frequency'):
self.training_frequency = cfg.getint('dqnpolicy', 'training_frequency')
# domain specific parameter settings (overrides general policy parameter settings)
if cfg.has_option('dqnpolicy_' + domainString, 'n_in'):
self.n_in = cfg.getint('dqnpolicy_' + domainString, 'n_in')
if cfg.has_option('dqnpolicy_' + domainString, 'actor_lr'):
self.actor_lr = cfg.getfloat('dqnpolicy_' + domainString, 'actor_lr')
if cfg.has_option('dqnpolicy_' + domainString, 'critic_lr'):
self.critic_lr = cfg.getfloat('dqnpolicy_' + domainString, 'critic_lr')
if cfg.has_option('dqnpolicy_' + domainString, 'tau'):
self.tau = cfg.getfloat('dqnpolicy_' + domainString, 'tau')
if cfg.has_option('dqnpolicy_' + domainString, 'gamma'):
self.gamma = cfg.getfloat('dqnpolicy_' + domainString, 'gamma')
if cfg.has_option('dqnpolicy_' + domainString, 'regularisation'):
self.regularisation = cfg.get('dqnpolicy_' + domainString, 'regulariser')
if cfg.has_option('dqnpolicy_' + domainString, 'learning_rate'):
self.learning_rate = cfg.getfloat('dqnpolicy_' + domainString, 'learning_rate')
if cfg.has_option('dqnpolicy_' + domainString, 'exploration_type'):
self.exploration_type = cfg.get('dqnpolicy_' + domainString, 'exploration_type')
if cfg.has_option('dqnpolicy_' + domainString, 'episodeNum'):
self.episodeNum = cfg.getfloat('dqnpolicy_' + domainString, 'episodeNum')
if cfg.has_option('dqnpolicy_' + domainString, 'maxiter'):
self.maxiter = cfg.getfloat('dqnpolicy_' + domainString, 'maxiter')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon'):
self.epsilon = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon_start')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon_end'):
self.epsilon_end = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon_end')
if cfg.has_option('dqnpolicy_' + domainString, 'prior_sample_prob_start'):
self.priorProbStart = cfg.getfloat('dqnpolicy_' + domainString, 'prior_sample_prob_start')
if cfg.has_option('dqnpolicy_' + domainString, 'prior_sample_prob_end'):
self.priorProbEnd = cfg.getfloat('dqnpolicy_' + domainString, 'prior_sample_prob_end')
if cfg.has_option('dqnpolicy_' + domainString, 'features'):
logger.info('Features: ' + str(cfg.get('dqnpolicy_' + domainString, 'features')))
self.policyfeatures = json.loads(cfg.get('dqnpolicy_' + domainString, 'features'))
if cfg.has_option('dqnpolicy_' + domainString, 'max_k'):
self.max_k = cfg.getint('dqnpolicy_' + domainString, 'max_k')
self.learning_algorithm = 'drl'
if cfg.has_option('dqnpolicy_' + domainString, 'learning_algorithm'):
self.learning_algorithm = cfg.get('dqnpolicy_' + domainString, 'learning_algorithm')
logger.info('Learning algorithm: ' + self.learning_algorithm)
if cfg.has_option('dqnpolicy_' + domainString, 'minibatch_size'):
self.minibatch_size = cfg.getint('dqnpolicy_' + domainString, 'minibatch_size')
if cfg.has_option('dqnpolicy_' + domainString, 'capacity'):
self.capacity = cfg.getint('dqnpolicy_' + domainString, 'capacity')
if cfg.has_option('dqnpolicy_' + domainString, 'replay_type'):
self.replay_type = cfg.get('dqnpolicy_' + domainString, 'replay_type')
if cfg.has_option('dqnpolicy_' + domainString, 'architecture'):
self.architecture = cfg.get('dqnpolicy_' + domainString, 'architecture')
if cfg.has_option('dqnpolicy_' + domainString, 'q_update'):
self.q_update = cfg.get('dqnpolicy_' + domainString, 'q_update')
if cfg.has_option('dqnpolicy_' + domainString, 'h1_size'):
self.h1_size = cfg.getint('dqnpolicy_' + domainString, 'h1_size')
if cfg.has_option('dqnpolicy_' + domainString, 'h2_size'):
self.h2_size = cfg.getint('dqnpolicy_' + domainString, 'h2_size')
if cfg.has_option('policy_' + domainString, 'save_step'):
self.save_step = cfg.getint('policy_' + domainString, 'save_step')
if cfg.has_option('dqnpolicy_' + domainString, 'importance_sampling'):
self.importance_sampling = cfg.get('dqnpolicy_' + domainString, 'importance_sampling')
if cfg.has_option('dqnpolicy_' + domainString, 'training_frequency'):
self.training_frequency = cfg.getint('dqnpolicy_' + domainString, 'training_frequency')
self.natural_gradient_prev = 0.
"""
self.shuffle = False
if cfg.has_option('dqnpolicy_'+domainString, 'experience_replay'):
self.shuffle = cfg.getboolean('dqnpolicy_'+domainString, 'experience_replay')
if not self.shuffle:
# If we don't use experience replay, we don't need to maintain
# sliding window of experiences with maximum capacity.
# We only need to maintain the data of minibatch_size
self.capacity = self.minibatch_size
"""
self.episode_ave_max_q = []
self.mu_prob = 0. # behavioral policy
os.environ["CUDA_VISIBLE_DEVICES"]=""
# init session
self.sess = tf.Session()
with tf.device("/cpu:0"):
np.random.seed(self.randomseed)
tf.set_random_seed(self.randomseed)
# initialise an replay buffer
if self.replay_type == 'vanilla':
self.episodes[self.domainString] = ReplayBufferEpisode(self.capacity, self.minibatch_size, self.randomseed)
elif self.replay_type == 'prioritized':
self.episodes[self.domainString] = ReplayPrioritisedEpisode(self.capacity, self.minibatch_size, self.randomseed)
#replay_buffer = ReplayBuffer(self.capacity, self.randomseed)
#self.episodes = []
self.samplecount = 0
self.episodecount = 0
# construct the models
self.state_dim = self.n_in
self.summaryaction = SummaryAction.SummaryAction(domainString)
self.action_dim = len(self.summaryaction.action_names)
action_bound = len(self.summaryaction.action_names)
self.stats = [0 for _ in range(self.action_dim)]
self.enac = enac.ENACNetwork(self.sess, self.state_dim, self.action_dim, \
self.critic_lr, self.tau, action_bound, self.architecture, self.h1_size, self.h2_size, self.is_training)
# when all models are defined, init all variables
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
self.loadPolicy(self.in_policy_file)
print 'loaded replay size: ', self.episodes[self.domainString].size()
def __init__(self, in_policy_file, out_policy_file, ontology, cfg, logger, SetObj, domainString='CamRestaurants', is_training=False):
super(STRACPolicy, self).__init__(domainString, ontology, cfg, logger, SetObj, is_training)
self.domainString = domainString
self.domainUtil = FlatOnt.FlatDomainOntology(self.domainString, cfg, ontology.OntologyUtils, SetObj)
self.in_policy_file = in_policy_file
self.out_policy_file = out_policy_file
self.is_training = is_training
self.accum_belief = []
self.prev_state_check = None
self.ontology = ontology
self.logger = logger
self.SetObj =SetObj
# parameter settings
if 0:#cfg.has_option('dqnpolicy', 'n_in'): #ic304: this was giving me a weird error, disabled it until i can check it deeper
self.n_in = cfg.getint('dqnpolicy', 'n_in')
else:
self.n_in = self.get_n_in(domainString)
self.actor_critic_combine = True
self.learning_rate = 0.001
if cfg.has_option('dqnpolicy', 'learning_rate'):
self.learning_rate = cfg.getfloat('dqnpolicy', 'learning_rate')
self.tau = 0.001
if cfg.has_option('dqnpolicy', 'tau'):
self.tau = cfg.getfloat('dqnpolicy', 'tau')
self.randomseed = 1234
if cfg.has_option('GENERAL', 'seed'):
self.randomseed = cfg.getint('GENERAL', 'seed')
self.gamma = 1.0
if cfg.has_option('dqnpolicy', 'gamma'):
self.gamma = cfg.getfloat('dqnpolicy', 'gamma')
self.gamma = 0.9
self.regularisation = 'l2'
if cfg.has_option('dqnpolicy', 'regularisation'):
self.regularisation = cfg.get('dqnpolicy', 'regulariser')
self.exploration_type = 'e-greedy' # Boltzman
if cfg.has_option('dqnpolicy', 'exploration_type'):
self.exploration_type = cfg.get('dqnpolicy', 'exploration_type')
self.episodeNum = 1000
if cfg.has_option('dqnpolicy', 'episodeNum'):
self.episodeNum = cfg.getfloat('dqnpolicy', 'episodeNum')
self.maxiter = 5000
if cfg.has_option('dqnpolicy', 'maxiter'):
self.maxiter = cfg.getfloat('dqnpolicy', 'maxiter')
self.maxiter += 2000
self.epsilon = 1
if cfg.has_option('dqnpolicy', 'epsilon'):
self.epsilon = cfg.getfloat('dqnpolicy', 'epsilon')
self.epsilon_start = 1
if cfg.has_option('dqnpolicy', 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy', 'epsilon_start')
# self.epsilon_start = 0.9
self.epsilon_end = 1
if cfg.has_option('dqnpolicy', 'epsilon_end'):
self.epsilon_end = cfg.getfloat('dqnpolicy', 'epsilon_end')
self.save_step = 100
if cfg.has_option('policy', 'save_step'):
self.save_step = cfg.getint('policy', 'save_step')
self.priorProbStart = 1.0
if cfg.has_option('dqnpolicy', 'prior_sample_prob_start'):
self.priorProbStart = cfg.getfloat('dqnpolicy', 'prior_sample_prob_start')
self.priorProbEnd = 0.1
if cfg.has_option('dqnpolicy', 'prior_sample_prob_end'):
self.priorProbEnd = cfg.getfloat('dqnpolicy', 'prior_sample_prob_end')
self.policyfeatures = []
if cfg.has_option('dqnpolicy', 'features'):
self.logger.info('Features: ' + str(cfg.get('dqnpolicy', 'features')))
self.policyfeatures = json.loads(cfg.get('dqnpolicy', 'features'))
self.max_k = 5
if cfg.has_option('dqnpolicy', 'max_k'):
self.max_k = cfg.getint('dqnpolicy', 'max_k')
self.learning_algorithm = 'drl'
if cfg.has_option('dqnpolicy', 'learning_algorithm'):
self.learning_algorithm = cfg.get('dqnpolicy', 'learning_algorithm')
self.logger.info('Learning algorithm: ' + self.learning_algorithm)
self.minibatch_size = 32
if cfg.has_option('dqnpolicy', 'minibatch_size'):
self.minibatch_size = cfg.getint('dqnpolicy', 'minibatch_size')
self.capacity = 1000 # max(self.minibatch_size, 2000)
if cfg.has_option('dqnpolicy', 'capacity'):
self.capacity = max(cfg.getint('dqnpolicy', 'capacity'), 2000)
self.replay_type = 'vanilla'
if cfg.has_option('dqnpolicy', 'replay_type'):
self.replay_type = cfg.get('dqnpolicy', 'replay_type')
self.architecture = 'vanilla'
if cfg.has_option('dqnpolicy', 'architecture'):
self.architecture = cfg.get('dqnpolicy', 'architecture')
self.q_update = 'double'
if cfg.has_option('dqnpolicy', 'q_update'):
self.q_update = cfg.get('dqnpolicy', 'q_update')
self.h1_size = 130
if cfg.has_option('dqnpolicy', 'h1_size'):
self.h1_size = cfg.getint('dqnpolicy', 'h1_size')
self.h1_drop = None
if cfg.has_option('dqnpolicy', 'h1_drop'):
self.h1_drop = cfg.getfloat('dqnpolicy', 'h1_drop')
self.h2_size = 130
if cfg.has_option('dqnpolicy', 'h2_size'):
self.h2_size = cfg.getint('dqnpolicy', 'h2_size')
self.h2_drop = None
if cfg.has_option('dqnpolicy', 'h2_drop'):
self.h2_drop = cfg.getfloat('dqnpolicy', 'h2_drop')
self.nature_mode = None
if cfg.has_option('dqnpolicy', 'nature_mode'):
self.nature_mode = cfg.getboolean('dqnpolicy', 'nature_mode')
self.madqn_hidden_layers = None
if cfg.has_option('dqnpolicy', 'madqn_hidden_layers'):
self.madqn_hidden_layers = cfg.getint('dqnpolicy', 'madqn_hidden_layers')
self.madqn_local_hidden_units = None
if cfg.has_option('dqnpolicy', 'madqn_local_hidden_units'):
self.madqn_local_hidden_units = cfg.get('dqnpolicy', 'madqn_local_hidden_units')
self.madqn_local_hidden_units = eval(self.madqn_local_hidden_units)
self.madqn_local_dropouts = None
if cfg.has_option('dqnpolicy', 'madqn_local_dropouts'):
self.madqn_local_dropouts = cfg.get('dqnpolicy', 'madqn_local_dropouts')
self.madqn_local_dropouts = eval(self.madqn_local_dropouts)
self.madqn_global_hidden_units = None
if cfg.has_option('dqnpolicy', 'madqn_global_hidden_units'):
self.madqn_global_hidden_units = cfg.get('dqnpolicy', 'madqn_global_hidden_units')
self.madqn_global_hidden_units = eval(self.madqn_global_hidden_units)
self.madqn_global_dropouts = None
if cfg.has_option('dqnpolicy', 'madqn_global_dropouts'):
self.madqn_global_dropouts = cfg.get('dqnpolicy', 'madqn_global_dropouts')
self.madqn_global_dropouts = eval(self.madqn_global_dropouts)
self.madqn_private_rate = None
if cfg.has_option('dqnpolicy', 'madqn_private_rate'):
self.madqn_private_rate = cfg.getfloat('dqnpolicy', 'madqn_private_rate')
self.madqn_sort_input_vec = False
if cfg.has_option('dqnpolicy', 'madqn_sort_input_vec'):
self.madqn_sort_input_vec = cfg.getboolean('dqnpolicy', 'madqn_sort_input_vec')
self.madqn_share_last_layer = False
if cfg.has_option('dqnpolicy', 'madqn_share_last_layer'):
self.madqn_share_last_layer = cfg.getboolean('dqnpolicy', 'madqn_share_last_layer')
self.madqn_shared_last_layer_use_bias = True
if cfg.has_option('dqnpolicy', 'madqn_shared_last_layer_use_bias'):
self.madqn_shared_last_layer_use_bias = cfg.getboolean('dqnpolicy', 'madqn_shared_last_layer_use_bias')
self.madqn_recurrent_mode = False
if cfg.has_option('dqnpolicy', 'madqn_recurrent_mode'):
self.madqn_recurrent_mode = cfg.getboolean('dqnpolicy', 'madqn_recurrent_mode')
self.madqn_input_comm = True
if cfg.has_option('dqnpolicy', 'madqn_input_comm'):
self.madqn_input_comm = cfg.getboolean('dqnpolicy', 'madqn_input_comm')
self.madqn_target_explore = False
if cfg.has_option('dqnpolicy', 'madqn_target_explore'):
self.madqn_target_explore = cfg.getboolean('dqnpolicy', 'madqn_target_explore')
self.madqn_concrete_share_rate = False
if cfg.has_option('dqnpolicy', 'madqn_concrete_share_rate'):
self.madqn_concrete_share_rate = cfg.getboolean('dqnpolicy', 'madqn_concrete_share_rate')
self.madqn_dropout_regularizer = 0.
if cfg.has_option('dqnpolicy', 'madqn_dropout_regularizer'):
self.madqn_dropout_regularizer = cfg.getfloat('dqnpolicy', 'madqn_dropout_regularizer')
self.madqn_weight_regularizer = 0.
if cfg.has_option('dqnpolicy', 'madqn_weight_regularizer'):
self.madqn_weight_regularizer = cfg.getfloat('dqnpolicy', 'madqn_weight_regularizer')
self.madqn_non_local_mode = False
if cfg.has_option('dqnpolicy', 'madqn_non_local_mode'):
self.madqn_non_local_mode = cfg.getboolean('dqnpolicy', 'madqn_non_local_mode')
self.madqn_block_mode = False
if cfg.has_option('dqnpolicy', 'madqn_block_mode'):
self.madqn_block_mode = cfg.getboolean('dqnpolicy', 'madqn_block_mode')
self.madqn_slots_comm = True
if cfg.has_option('dqnpolicy', 'madqn_slots_comm'):
self.madqn_slots_comm = cfg.getboolean('dqnpolicy', 'madqn_slots_comm')
self.madqn_use_dueling = False
if cfg.has_option('dqnpolicy', 'madqn_use_dueling'):
self.madqn_use_dueling = cfg.getboolean('dqnpolicy', 'madqn_use_dueling')
self.madqn_topo_learning_mode = False
if cfg.has_option('dqnpolicy', 'madqn_topo_learning_mode'):
self.madqn_topo_learning_mode = cfg.getboolean('dqnpolicy', 'madqn_topo_learning_mode')
self.madqn_message_embedding = False
if cfg.has_option('dqnpolicy', 'madqn_message_embedding'):
self.madqn_message_embedding = cfg.getboolean('dqnpolicy', 'madqn_message_embedding')
self.madqn_dueling_share_last = False
if cfg.has_option('dqnpolicy', 'madqn_dueling_share_last'):
self.madqn_dueling_share_last = cfg.getboolean('dqnpolicy', 'madqn_dueling_share_last')
self.state_feature = 'vanilla'
if cfg.has_option('dqnpolicy', 'state_feature'):
self.state_feature = cfg.get('dqnpolicy', 'state_feature')
self.init_policy = None
if cfg.has_option('dqnpolicy', 'init_policy'):
self.init_policy = cfg.get('dqnpolicy', 'init_policy')
self.training_frequency = 2
if cfg.has_option('dqnpolicy', 'training_frequency'):
self.training_frequency = cfg.getint('dqnpolicy', 'training_frequency')
self.importance_sampling = True
if cfg.has_option('dqnpolicy', 'importance_sampling'):
self.importance_sampling = cfg.getint('dqnpolicy', 'importance_sampling')
# domain specific parameter settings (overrides general policy parameter settings)
if cfg.has_option('dqnpolicy_' + domainString, 'n_in'):
self.n_in = cfg.getint('dqnpolicy_' + domainString, 'n_in')
if cfg.has_option('dqnpolicy_' + domainString, 'learning_rate'):
self.learning_rate = cfg.getfloat('dqnpolicy_' + domainString, 'learning_rate')
if cfg.has_option('dqnpolicy_' + domainString, 'tau'):
self.tau = cfg.getfloat('dqnpolicy_' + domainString, 'tau')
if cfg.has_option('dqnpolicy_' + domainString, 'gamma'):
self.gamma = cfg.getfloat('dqnpolicy_' + domainString, 'gamma')
if cfg.has_option('dqnpolicy_' + domainString, 'regularisation'):
self.regularisation = cfg.get('dqnpolicy_' + domainString, 'regulariser')
if cfg.has_option('dqnpolicy_' + domainString, 'exploration_type'):
self.exploration_type = cfg.get('dqnpolicy_' + domainString, 'exploration_type')
if cfg.has_option('dqnpolicy_' + domainString, 'episodeNum'):
self.episodeNum = cfg.getfloat('dqnpolicy_' + domainString, 'episodeNum')
if cfg.has_option('dqnpolicy_' + domainString, 'maxiter'):
self.maxiter = cfg.getfloat('dqnpolicy_' + domainString, 'maxiter')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon'):
self.epsilon = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon_start'):
self.epsilon_start = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon_start')
if cfg.has_option('dqnpolicy_' + domainString, 'epsilon_end'):
self.epsilon_end = cfg.getfloat('dqnpolicy_' + domainString, 'epsilon_end')
if cfg.has_option('policy_' + domainString, 'save_step'):
self.save_step = cfg.getint('policy_' + domainString, 'save_step')
if cfg.has_option('dqnpolicy_' + domainString, 'prior_sample_prob_start'):
self.priorProbStart = cfg.getfloat('dqnpolicy_' + domainString, 'prior_sample_prob_start')
if cfg.has_option('dqnpolicy_' + domainString, 'prior_sample_prob_end'):
self.priorProbEnd = cfg.getfloat('dqnpolicy_' + domainString, 'prior_sample_prob_end')
if cfg.has_option('dqnpolicy_' + domainString, 'features'):
self.logger.info('Features: ' + str(cfg.get('dqnpolicy_' + domainString, 'features')))
self.policyfeatures = json.loads(cfg.get('dqnpolicy_' + domainString, 'features'))
if cfg.has_option('dqnpolicy_' + domainString, 'max_k'):
self.max_k = cfg.getint('dqnpolicy_' + domainString, 'max_k')
if cfg.has_option('dqnpolicy_' + domainString, 'learning_algorithm'):
self.learning_algorithm = cfg.get('dqnpolicy_' + domainString, 'learning_algorithm')
self.logger.info('Learning algorithm: ' + self.learning_algorithm)
if cfg.has_option('dqnpolicy_' + domainString, 'minibatch_size'):
self.minibatch_size = cfg.getint('dqnpolicy_' + domainString, 'minibatch_size')
if cfg.has_option('dqnpolicy_' + domainString, 'capacity'):
self.capacity = max(cfg.getint('dqnpolicy_' + domainString, 'capacity'), 2000)
if cfg.has_option('dqnpolicy_' + domainString, 'replay_type'):
self.replay_type = cfg.get('dqnpolicy_' + domainString, 'replay_type')
if cfg.has_option('dqnpolicy_' + domainString, 'architecture'):
self.architecture = cfg.get('dqnpolicy_' + domainString, 'architecture')
if cfg.has_option('dqnpolicy_' + domainString, 'q_update'):
self.q_update = cfg.get('dqnpolicy_' + domainString, 'q_update')
if cfg.has_option('dqnpolicy_' + domainString, 'h1_size'):
self.h1_size = cfg.getint('dqnpolicy_' + domainString, 'h1_size')
if cfg.has_option('dqnpolicy_' + domainString, 'h1_drop'):
self.h1_drop = cfg.getfloat('dqnpolicy_' + domainString, 'h1_drop')
if cfg.has_option('dqnpolicy_' + domainString, 'h2_size'):
self.h2_size = cfg.getint('dqnpolicy_' + domainString, 'h2_size')
if cfg.has_option('dqnpolicy_' + domainString, 'h2_drop'):
self.h2_drop = cfg.getfloat('dqnpolicy_' + domainString, 'h2_drop')
if cfg.has_option('dqnpolicy_' + domainString, 'training_frequency'):
self.training_frequency = cfg.getint('dqnpolicy_' + domainString, 'training_frequency')
"""
self.shuffle = False
if cfg.has_option('dqnpolicy_'+domainString, 'experience_replay'):
self.shuffle = cfg.getboolean('dqnpolicy_'+domainString, 'experience_replay')
if not self.shuffle:
# If we don't use experience replay, we don't need to maintain
# sliding window of experiences with maximum capacity.
# We only need to maintain the data of minibatch_size
self.capacity = self.minibatch_size
"""
self.episode_ave_max_q = []
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# init session
# self.sess = tf.Session()
# with tf.device("/cpu:0"):
np.random.seed(self.randomseed)
# tf.set_random_seed(self.randomseed)
# initialise an replay buffer
if self.replay_type == 'vanilla':
self.episodes[self.domainString] = ReplayBufferEpisode(self.capacity, self.minibatch_size, self.randomseed)
elif self.replay_type == 'prioritized':
self.episodes[self.domainString] = ReplayPrioritisedEpisode(self.capacity, self.minibatch_size, self.randomseed)
self.samplecount = 0
self.episodecount = 0
# construct the models
self.state_dim = self.n_in
self.summaryaction = SummaryAction.SummaryAction(domainString, self.ontology, self.SetObj)
self.action_dim = len(self.summaryaction.action_names)
action_bound = len(self.summaryaction.action_names)
self.stats = [0 for _ in range(self.action_dim)]
import tube
self.strac = strac.STRACNetwork(self.state_dim, self.action_dim, \
self.learning_rate, self.tau, action_bound, self.minibatch_size,
self.architecture, self.h1_size, self.h1_drop,
self.h2_size, self.h2_drop, self.domainString,
self.madqn_hidden_layers,
self.madqn_local_hidden_units, self.madqn_local_dropouts,
self.madqn_global_hidden_units, self.madqn_global_dropouts,
self.madqn_private_rate, self.madqn_sort_input_vec,
self.madqn_share_last_layer, self.madqn_recurrent_mode,
self.madqn_input_comm, self.madqn_target_explore,
concrete_share_rate=self.madqn_concrete_share_rate,
dropout_regularizer=self.madqn_dropout_regularizer,
weight_regularizer=self.madqn_weight_regularizer,
non_local_mode=self.madqn_non_local_mode,
block_mode=self.madqn_block_mode,
slots_comm=self.madqn_slots_comm,
topo_learning_mode=self.madqn_topo_learning_mode,
use_dueling=self.madqn_use_dueling,
dueling_share_last=self.madqn_dueling_share_last,
message_embedding=self.madqn_message_embedding,
state_feature=self.state_feature,
init_policy=self.init_policy,
shared_last_layer_use_bias=self.madqn_shared_last_layer_use_bias,
seed=tube.seed)
lock.acquire()
self.loadPolicy(self.in_policy_file)
self.savePolicyInc()
lock.release()
print(self.domainString + ' loaded replay size: ' + str(self.episodes[self.domainString].size()))
Settings.load_policy(self.strac, threading.currentThread().getName())