API - 强化学习¶
强化学习（增强学习）相关函数。
discount_episode_rewards([rewards, gamma, mode]) |
Take 1D float array of rewards and compute discounted rewards for an episode. |
cross_entropy_reward_loss(logits, actions, ...) |
Calculate the loss for Policy Gradient Network. |
log_weight(probs, weights[, name]) |
Log weight. |
choice_action_by_probs([probs, action_list]) |
Choice and return an an action by given the action probability distribution. |
奖励函数¶
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tensorlayer.rein.discount_episode_rewards(rewards=[], gamma=0.99, mode=0)[源代码]¶ -
Take 1D float array of rewards and compute discounted rewards for an
episode. When encount a non-zero value, consider as the end a of an episode.Parameters: rewards : numpy list
a list of rewards
gamma : float
discounted factor
mode : int
if mode == 0, reset the discount process when encount a non-zero reward (Ping-pong game).
if mode == 1, would not reset the discount process.Examples
>>> rewards = np.asarray([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1])
>>> gamma = 0.9
>>> discount_rewards = tl.rein.discount_episode_rewards(rewards, gamma)
>>> print(discount_rewards)
... [ 0.72899997 0.81 0.89999998 1. 0.72899997 0.81
... 0.89999998 1. 0.72899997 0.81 0.89999998 1. ]
>>> discount_rewards = tl.rein.discount_episode_rewards(rewards, gamma, mode=1)
>>> print(discount_rewards)
... [ 1.52110755 1.69011939 1.87791049 2.08656716 1.20729685 1.34144104
... 1.49048996 1.65610003 0.72899997 0.81 0.89999998 1. ]
损失函数¶
Weighted Cross Entropy¶
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tensorlayer.rein.cross_entropy_reward_loss(logits, actions, rewards, name=None)[源代码]¶ -
Calculate the loss for Policy Gradient Network.
Parameters: logits : tensor
The network outputs without softmax. This function implements softmax
inside.actions : tensor/ placeholder
The agent actions.
rewards : tensor/ placeholder
The rewards.
Examples
>>> states_batch_pl = tf.placeholder(tf.float32, shape=[None, D])
>>> network = InputLayer(states_batch_pl, name='input')
>>> network = DenseLayer(network, n_units=H, act=tf.nn.relu, name='relu1')
>>> network = DenseLayer(network, n_units=3, name='out')
>>> probs = network.outputs
>>> sampling_prob = tf.nn.softmax(probs)
>>> actions_batch_pl = tf.placeholder(tf.int32, shape=[None])
>>> discount_rewards_batch_pl = tf.placeholder(tf.float32, shape=[None])
>>> loss = tl.rein.cross_entropy_reward_loss(probs, actions_batch_pl, discount_rewards_batch_pl)
>>> train_op = tf.train.RMSPropOptimizer(learning_rate, decay_rate).minimize(loss)
Log weight¶
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tensorlayer.rein.log_weight(probs, weights, name='log_weight')[源代码]¶ -
Log weight.
Parameters: probs : tensor
If it is a network output, usually we should scale it to [0, 1] via softmax.
weights : tensor
采样选择函数¶
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tensorlayer.rein.choice_action_by_probs(probs=[0.5, 0.5], action_list=None)[源代码]¶ -
Choice and return an an action by given the action probability distribution.
Parameters: probs : a list of float.
The probability distribution of all actions.
action_list : None or a list of action in integer, string or others.
If None, returns an integer range between 0 and len(probs)-1.
Examples
>>> for _ in range(5):
>>> a = choice_action_by_probs([0.2, 0.4, 0.4])
>>> print(a)
... 0
... 1
... 1
... 2
... 1
>>> for _ in range(3):
>>> a = choice_action_by_probs([0.5, 0.5], ['a', 'b'])
>>> print(a)
... a
... b
... b
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