# TensorFlow Reinforcement Learning

## TRFL

TRFL (pronounced "truffle") is a library built on top of TensorFlow that exposes

several useful building blocks for implementing Reinforcement Learning agents.

## Installation

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.

## GitHub

### Comments

### Subscribe to Python Awesome

Get the latest posts delivered right to your inbox