API - Reinforcement Learning¶
Reinforcement Learning.
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. |
Reward functions¶
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tensorlayer.rein.
discount_episode_rewards
(rewards=[], gamma=0.99, mode=0)[source]¶ 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. ]
Cost functions¶
Weighted Cross Entropy¶
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tensorlayer.rein.
cross_entropy_reward_loss
(logits, actions, rewards, name=None)[source]¶ 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)
Sampling functions¶
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tensorlayer.rein.
choice_action_by_probs
(probs=[0.5, 0.5], action_list=None)[source]¶ 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