/ Machine Learning

TensorFlow Reinforcement Learning

TensorFlow Reinforcement Learning


TRFL (pronounced "truffle") is a library built on top of TensorFlow that exposes
several useful building blocks for implementing Reinforcement Learning agents.


TRFL can be installed from pip directly from github, with the following command:
pip install git+git://github.com/deepmind/trfl.git

TRFL will work with both the CPU and GPU version of tensorflow, but to allow
for that it does not list Tensorflow as a requirement, so you need to install
Tensorflow and Tensorflow-probability separately if you haven't already done so.

Usage Example

import tensorflow as tf
import trfl

# Q-values for the previous and next timesteps, shape [batch_size, num_actions].
q_tm1 = tf.get_variable(
    "q_tm1", initializer=[[1., 1., 0.], [1., 2., 0.]], dtype=tf.float32)
q_t = tf.get_variable(
    "q_t", initializer=[[0., 1., 0.], [1., 2., 0.]], dtype=tf.float32)

# Action indices, discounts and rewards, shape [batch_size].
a_tm1 = tf.constant([0, 1], dtype=tf.int32)
r_t = tf.constant([1, 1], dtype=tf.float32)
pcont_t = tf.constant([0, 1], dtype=tf.float32)  # the discount factor

# Q-learning loss, and auxiliary data.
loss, q_learning = trfl.qlearning(q_tm1, a_tm1, r_t, pcont_t, q_t)

loss is the tensor representing the loss. For Q-learning, it is half the
squared difference between the predicted Q-values and the TD targets, shape
[batch_size]. Extra information is in the q_learning namedtuple, including
q_learning.td_error and q_learning.target.

The loss tensor can be differentiated to derive the corresponding RL update.

reduced_loss = tf.reduce_mean(loss)
optimizer = tf.train.AdamOptimizer(learning_rate=0.1)
train_op = optimizer.minimize(reduced_loss)

All loss functions in the package return both a loss tensor and a namedtuple
with extra information, using the above convention, but different functions
may have different extra fields. Check the documentation of each function
below for more information.