neural_q_learner.py 文件源码

def __init__(self, session,
                     optimizer,
                     q_network,
                     state_dim,
                     num_actions,
                     batch_size=32,
                     init_exp=0.5,       # initial exploration prob
                     final_exp=0.1,      # final exploration prob
                     anneal_steps=10000, # N steps for annealing exploration
                     replay_buffer_size=10000,
                     store_replay_every=5, # how frequent to store experience
                     discount_factor=0.9, # discount future rewards
                     target_update_rate=0.01,
                     reg_param=0.01, # regularization constants
                     max_gradient=5, # max gradient norms
                     double_q_learning=False,
                     summary_writer=None,
                     summary_every=100):

    # tensorflow machinery
    self.session        = session
    self.optimizer      = optimizer
    self.summary_writer = summary_writer

    # model components
    self.q_network     = q_network
    self.replay_buffer = ReplayBuffer(buffer_size=replay_buffer_size)

    # Q learning parameters
    self.batch_size      = batch_size
    self.state_dim       = state_dim
    self.num_actions     = num_actions
    self.exploration     = init_exp
    self.init_exp        = init_exp
    self.final_exp       = final_exp
    self.anneal_steps    = anneal_steps
    self.discount_factor = discount_factor
    self.target_update_rate = target_update_rate
    self.double_q_learning = double_q_learning

    # training parameters
    self.max_gradient = max_gradient
    self.reg_param    = reg_param

    # counters
    self.store_replay_every   = store_replay_every
    self.store_experience_cnt = 0
    self.train_iteration      = 0

    # create and initialize variables
    self.create_variables()
    var_lists = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
    self.session.run(tf.variables_initializer(var_lists))

    # make sure all variables are initialized
    self.session.run(tf.assert_variables_initialized())

    if self.summary_writer is not None:
      # graph was not available when journalist was created
      self.summary_writer.add_graph(self.session.graph)
      self.summary_every = summary_every