111 lines
4.4 KiB
Python
111 lines
4.4 KiB
Python
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import tensorflow as tf
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from .base import PolicyBase
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class Deterministic(PolicyBase):
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"""
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deterministic policy as used in deterministic policy gradient methods
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"""
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def __init__(self, policy_callable, observation_placeholder, weight_update=1):
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self._observation_placeholder = observation_placeholder
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self.managed_placeholders = {'observation': observation_placeholder}
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self.weight_update = weight_update
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self.interaction_count = -1 # defaults to -1. only useful if weight_update > 1.
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# build network, action and value
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with tf.variable_scope('network', reuse=tf.AUTO_REUSE):
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action, _ = policy_callable()
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self.action = action
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# TODO: self._action should be exactly the action tensor to run that directly gives action_dim
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self.trainable_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='network')
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# deal with target network
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if self.weight_update == 1:
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self.weight_update_ops = None
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self.sync_weights_ops = None
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else: # then we need to build another tf graph as target network
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with tf.variable_scope('net_old', reuse=tf.AUTO_REUSE):
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action, _ = policy_callable()
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self.action_old = action
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network_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='network')
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network_old_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='net_old')
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# TODO: use a scope that the user will almost surely not use. so get_collection will return
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# the correct weights and old_weights, since it filters by regular expression
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# or we write a util to parse the variable names and use only the topmost scope
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assert len(network_weights) == len(network_old_weights)
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self.sync_weights_ops = [tf.assign(variable_old, variable)
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for (variable_old, variable) in zip(network_old_weights, network_weights)]
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if weight_update == 0:
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self.weight_update_ops = self.sync_weights_ops
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elif 0 < weight_update < 1: # as in DDPG
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self.weight_update_ops = [tf.assign(variable_old,
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weight_update * variable + (1 - weight_update) * variable_old)
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for (variable_old, variable) in zip(network_old_weights, network_weights)]
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else:
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self.interaction_count = 0 # as in DQN
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import math
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self.weight_update = math.ceil(weight_update)
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@property
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def action_shape(self):
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return self.action.shape.as_list()[1:]
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def act(self, observation, my_feed_dict={}):
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# TODO: this may be ugly. also maybe huge problem when parallel
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sess = tf.get_default_session()
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# observation[None] adds one dimension at the beginning
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feed_dict = {self._observation_placeholder: observation[None]}
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feed_dict.update(my_feed_dict)
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sampled_action = sess.run(self.action, feed_dict=feed_dict)
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sampled_action = sampled_action[0]
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return sampled_action
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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.weight_update_ops is not None:
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sess = tf.get_default_session()
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sess.run(self.weight_update_ops)
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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.sync_weights_ops is not None:
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sess = tf.get_default_session()
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sess.run(self.sync_weights_ops)
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def eval_action(self, observation):
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"""
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evaluate action in minibatch
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:param observation:
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:return: 2-D numpy array
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"""
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sess = tf.get_default_session()
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feed_dict = {self._observation_placeholder: observation}
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action = sess.run(self.action, feed_dict=feed_dict)
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return action
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def eval_action_old(self, observation):
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"""
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evaluate action in minibatch
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:param observation:
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:return: 2-D numpy array
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"""
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sess = tf.get_default_session()
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feed_dict = {self._observation_placeholder: observation}
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action = sess.run(self.action_old, feed_dict=feed_dict)
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return action
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