54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
from __future__ import absolute_import
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from .base import PolicyBase
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import tensorflow as tf
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import numpy as np
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class DQN(PolicyBase):
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"""
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use DQN from value_function as a member
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"""
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def __init__(self, dqn):
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self.action_value = dqn
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self._argmax_action = tf.argmax(dqn.value_tensor_all_actions, axis=1)
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self.weight_update = dqn.weight_update
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if self.weight_update > 1:
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self.interaction_count = 0
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else:
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self.interaction_count = -1
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def act(self, observation, exploration=None):
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sess = tf.get_default_session()
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if self.weight_update > 1:
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if self.interaction_count % self.weight_update == 0:
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self.update_weights()
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feed_dict = {self.action_value._observation_placeholder: observation[None]}
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action = sess.run(self._argmax_action, feed_dict=feed_dict)
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if self.weight_update > 0:
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self.interaction_count += 1
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if not exploration:
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return np.squeeze(action)
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@property
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def q_net(self):
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return self.action_value
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def sync_weights(self):
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"""
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sync the weights of network_old. Direct copy the weights of network.
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:return:
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"""
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if self.action_value.sync_weights_ops is not None:
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self.action_value.sync_weights()
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def update_weights(self):
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"""
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updates the weights of policy_old.
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:return:
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"""
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if self.action_value.weight_update_ops is not None:
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self.action_value.update_weights() |