本文整理匯總了Python中baselines.common.tf_util.huber_loss方法的典型用法代碼示例。如果您正苦於以下問題：Python tf_util.huber_loss方法的具體用法？Python tf_util.huber_loss怎麽用？Python tf_util.huber_loss使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類baselines.common.tf_util的用法示例。
在下文中一共展示了tf_util.huber_loss方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: qmap_build_train
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import huber_loss [as 別名]
def qmap_build_train(observation_space, coords_shape, model, n_actions, optimizer, grad_norm_clip, scope='q_map'):
with tf.variable_scope(scope):
ob_shape = observation_space.shape
observations = tf.placeholder(tf.float32, [None] + list(ob_shape), name='observations')
actions = tf.placeholder(tf.int32, [None], name='actions')
target_qs = tf.placeholder(tf.float32, [None] + list(coords_shape), name='targets')
weights = tf.placeholder(tf.float32, [None], name='weights')
q_values = model(inpt=observations, n_actions=n_actions, scope='q_func')
q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/q_func")
target_q_values = model(inpt=observations, n_actions=n_actions, scope='target_q_func')
target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/target_q_func")
action_masks = tf.expand_dims(tf.expand_dims(tf.one_hot(actions, n_actions), axis=1), axis=1)
qs_selected = tf.reduce_sum(q_values * action_masks, 3)
td_errors = 1 * (qs_selected - target_qs) # TODO: coefficient?
losses = tf.reduce_mean(tf.square(td_errors), [1, 2]) # TODO: find best, was U.huber_loss
weighted_loss = tf.reduce_mean(weights * losses)
if grad_norm_clip is not None:
gradients = optimizer.compute_gradients(weighted_loss, var_list=q_func_vars)
for i, (grad, var) in enumerate(gradients):
if grad is not None:
gradients[i] = (tf.clip_by_norm(grad, grad_norm_clip), var)
optimize = optimizer.apply_gradients(gradients)
grad_norms = [tf.norm(grad) for grad in gradients]
else:
optimize = optimizer.minimize(weighted_loss, var_list=q_func_vars)
grad_norms = None
update_target_expr = []
for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_expr.append(var_target.assign(var))
update_target_expr = tf.group(*update_target_expr)
errors = tf.reduce_mean(tf.abs(td_errors), [1, 2]) # TODO: try with the losses directly
compute_q_values = U.function(inputs=[observations], outputs=q_values)
compute_double_q_values = U.function(inputs=[observations], outputs=[q_values, target_q_values])
train = U.function(inputs=[observations, actions, target_qs, weights], outputs=errors, updates=[optimize])
update_target = U.function([], [], updates=[update_target_expr])
trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)
train_debug = U.function(inputs=[observations, actions, target_qs, weights], outputs=[errors, weighted_loss, grad_norms, trainable_vars], updates=[optimize])
return compute_q_values, compute_double_q_values, train, update_target, train_debug